Variants and gene fusions

Fig 5 Variants and fusions

Variants

ALS vs CTRL variant number

answerals.vcfannotate.samples_no_variants = answerals.metadata$dataset_sample[!answerals.metadata$dataset_sample %in% answerals.vcfannotate$dataset_sample] # 0 i.e. all samples have at least 1 variant
answerals.vcfannotate.totals = answerals.vcfannotate %>% distinct(dataset_sample, .keep_all = TRUE) #keep 1 row per sample for total number of variants

# generalised linear model spline
answerals.vcfannotate.totals.glm = glm(data=answerals.vcfannotate.totals, total ~ CONDITION + rms::rcs(total_reads, 3), family = poisson)
summary(answerals.vcfannotate.totals.glm)

## 
## Call:
## glm(formula = total ~ CONDITION + rms::rcs(total_reads, 3), family = poisson, 
##     data = answerals.vcfannotate.totals)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -20.100   -6.822   -2.358    2.699   48.785  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           1.063e+01  3.409e-03 3119.19   <2e-16 ***
## CONDITIONALS                          1.020e-02  7.027e-04   14.52   <2e-16 ***
## rms::rcs(total_reads, 3)total_reads   4.116e-09  4.353e-11   94.54   <2e-16 ***
## rms::rcs(total_reads, 3)total_reads' -2.174e-09  5.152e-11  -42.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 54707  on 279  degrees of freedom
## Residual deviance: 36003  on 276  degrees of freedom
## AIC: 39600
## 
## Number of Fisher Scoring iterations: 3

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           1.063e+01  3.409e-03 3119.19   <2e-16 ***
# CONDITIONALS                          1.020e-02  7.027e-04   14.52   <2e-16 ***

# Violin total variant events
answerals.vcfannotate.totals.stats = tibble(group1 = "CTRL", group2 = "ALS", p.signif = "****", y.position = 75000, xmin = 1, xmax = 2)
answerals.vcfannotate.total.violin <- violin_plot(answerals.vcfannotate.totals, color_by = "CONDITION", continuous = "total", cols = c("dodgerblue2", "firebrick2"), one_sample = FALSE, plot_stats = FALSE, arrow_labels = FALSE, ylims = c(50000,76000), dpi = 72, ylabel = "Total Number of Variants", title = "All Variant types") +
  stat_pvalue_manual(answerals.vcfannotate.totals.stats, tip.length = 0.01, size = 4)
answerals.vcfannotate.total.violin

Variant types

SNP

answerals.vcfannotate.snp = answerals.vcfannotate %>% filter(variant_type == "SNP") 
# add samples with 0 variants for each variant type
samples_no_SNP = answerals.metadata$dataset_sample[!answerals.metadata$dataset_sample %in% pull(answerals.vcfannotate.snp,dataset_sample)] 
answerals.vcfannotate.snp.glm = glm(data = answerals.vcfannotate.snp, n ~ CONDITION + rms::rcs(total_reads, 3), family = poisson)
summary(answerals.vcfannotate.snp.glm)

## 
## Call:
## glm(formula = n ~ CONDITION + rms::rcs(total_reads, 3), family = poisson, 
##     data = answerals.vcfannotate.snp)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -18.267   -6.464   -2.250    2.197   46.466  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           1.048e+01  3.680e-03 2847.29   <2e-16 ***
## CONDITIONALS                          8.880e-03  7.581e-04   11.71   <2e-16 ***
## rms::rcs(total_reads, 3)total_reads   4.164e-09  4.700e-11   88.61   <2e-16 ***
## rms::rcs(total_reads, 3)total_reads' -2.299e-09  5.564e-11  -41.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 48499  on 279  degrees of freedom
## Residual deviance: 32867  on 276  degrees of freedom
## AIC: 36421
## 
## Number of Fisher Scoring iterations: 3

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           1.048e+01  3.680e-03 2847.29   <2e-16 ***
# CONDITIONALS                          8.880e-03  7.581e-04   11.71   <2e-16 ***
answerals.vcfannotate.snp.stats = tibble(group1 = "CTRL", group2 = "ALS", p.signif = "****", y.position = 62000, xmin = 1, xmax = 2)
answerals.vcfannotate.snp.violin <- violin_plot(answerals.vcfannotate.snp, color_by = "CONDITION", continuous = "n", cols = c("dodgerblue2", "firebrick2"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(40000,64000), dpi = 72, ylabel = "Number of Variants", title = "SNV")  +
  stat_pvalue_manual(answerals.vcfannotate.snp.stats, tip.length = 0.01, size = 4)
answerals.vcfannotate.snp.violin

Insertions

answerals.vcfannotate.insertion = answerals.vcfannotate %>% filter(variant_type == "Insertion") 
# add samples with 0 variants for each variant type
samples_no_Insertion = answerals.metadata$dataset_sample[!answerals.metadata$dataset_sample %in% pull(answerals.vcfannotate.insertion,dataset_sample)]
answerals.vcfannotate.Insertion.glm = glm(data = answerals.vcfannotate.insertion, n ~ CONDITION + rms::rcs(total_reads, 3), family = poisson)
summary(answerals.vcfannotate.Insertion.glm)

## 
## Call:
## glm(formula = n ~ CONDITION + rms::rcs(total_reads, 3), family = poisson, 
##     data = answerals.vcfannotate.insertion)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -15.4957   -2.8982   -0.3836    3.0568   20.8196  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           8.121e+00  1.219e-02 666.031  < 2e-16 ***
## CONDITIONALS                          1.928e-02  2.528e-03   7.624 2.47e-14 ***
## rms::rcs(total_reads, 3)total_reads   3.391e-09  1.557e-10  21.781  < 2e-16 ***
## rms::rcs(total_reads, 3)total_reads' -6.114e-10  1.837e-10  -3.329 0.000873 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 9242.2  on 279  degrees of freedom
## Residual deviance: 7330.3  on 276  degrees of freedom
## AIC: 10212
## 
## Number of Fisher Scoring iterations: 3

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           8.121e+00  1.219e-02 666.031  < 2e-16 ***
# CONDITIONALS                          1.928e-02  2.528e-03   7.624 2.47e-14 ***

answerals.vcfannotate.insertion.stats = tibble(group1 = "CTRL", group2 = "ALS", p.signif = "****", y.position = 6400, xmin = 1, xmax = 2)
answerals.vcfannotate.insertion.violin <- violin_plot(answerals.vcfannotate.insertion, color_by = "CONDITION", continuous = "n", cols = c("dodgerblue2", "firebrick2"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(3500,6700), dpi = 72, ylabel = "", title = "Insertion")  +
  stat_pvalue_manual(answerals.vcfannotate.insertion.stats, tip.length = 0.01, size = 4)
answerals.vcfannotate.insertion.violin

Deletions

answerals.vcfannotate.deletion = answerals.vcfannotate %>% filter(variant_type == "Deletion") 
samples_no_Deletion = answerals.metadata$dataset_sample[!answerals.metadata$dataset_sample %in% pull(answerals.vcfannotate.deletion,dataset_sample)] 
answerals.vcfannotate.Deletion.glm = glm(data = answerals.vcfannotate.deletion, n ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
summary(answerals.vcfannotate.Deletion.glm)

## 
## Call:
## glm(formula = n ~ CONDITION + rms::rcs(total_reads, 3), family = poisson, 
##     data = answerals.vcfannotate.deletion)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.5241  -1.4909  -0.2659   0.8584   9.7615  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           7.858e+00  1.351e-02  581.45  < 2e-16 ***
## CONDITIONALS                          1.701e-02  2.789e-03    6.10 1.06e-09 ***
## rms::rcs(total_reads, 3)total_reads   4.337e-09  1.726e-10   25.13  < 2e-16 ***
## rms::rcs(total_reads, 3)total_reads' -2.406e-09  2.041e-10  -11.79  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 3408.5  on 279  degrees of freedom
## Residual deviance: 2102.7  on 276  degrees of freedom
## AIC: 4929
## 
## Number of Fisher Scoring iterations: 3

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           7.858e+00  1.351e-02  581.45  < 2e-16 ***
# CONDITIONALS                          1.701e-02  2.789e-03    6.10 1.06e-09 ***
answerals.vcfannotate.insertion.stats = tibble(group1 = "CTRL", group2 = "ALS", p.signif = "****", y.position = 4900, xmin = 1, xmax = 2)
answerals.vcfannotate.deletion.violin <- violin_plot(answerals.vcfannotate.deletion, color_by = "CONDITION", continuous = "n", cols = c("dodgerblue2", "firebrick2"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(3000,5000), dpi = 72, ylabel = "", title = "Deletion") +
  stat_pvalue_manual(answerals.vcfannotate.insertion.stats, tip.length = 0.01, size = 4)
answerals.vcfannotate.deletion.violin

Substitutions

library(MutationalPatterns)
ref_genome <- "BSgenome.Hsapiens.UCSC.hg38"
library(ref_genome, character.only = TRUE)
type_occurrences = readRDS(here(shared_path, "answerals/rnavar/mutationalpatterns/type_occurrences.RDS"))
answerals.multiqc_rseqc_read_distribution.tsv = read_tsv(here(shared_path,"answerals/nfcore/multiqc/star_salmon/multiqc_data/multiqc_rseqc_read_distribution.txt"), show_col_types = FALSE) %>% clean_names()
answerals.base_substitutions = type_occurrences %>% as_tibble(rownames = "sample") %>% select(-`C>T at CpG`, -`C>T other`) %>%
  left_join(select(answerals.metadata, sample, condition)) %>% 
  pivot_longer(!c(condition, sample), names_to = "Substitution", values_to = "count") %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), Substitution = as.factor(Substitution)) %>%
  left_join(select(answerals.multiqc_rseqc_read_distribution.tsv, sample, total_reads))

# ### C>A
# glm_starfusion_C_A = glm(data = filter(answerals.base_substitutions, Substitution == "C>A"), count ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion_C_A)
# # Coefficients:
# #                                        Estimate Std. Error z value Pr(>|z|)    
# # (Intercept)                           7.672e+00  1.443e-02 531.861  < 2e-16 ***
# # CONDITIONALS                          8.564e-03  2.952e-03   2.901  0.00372 ** 
# 
# ### C>G
# glm_starfusion_C_G = glm(data = filter(answerals.base_substitutions, Substitution == "C>G"), count ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion_C_G)
# # Coefficients:
# #                                        Estimate Std. Error z value Pr(>|z|)    
# # (Intercept)                           8.085e+00  1.230e-02 657.069   <2e-16 ***
# # CONDITIONALS                          3.246e-03  2.533e-03   1.281      0.2    
# 
# ### C>T
# glm_starfusion_C_T = glm(data = filter(answerals.base_substitutions, Substitution == "C>T"), count ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion_C_T)
# # Coefficients:
# #                                        Estimate Std. Error  z value Pr(>|z|)    
# # (Intercept)                           9.365e+00  6.467e-03 1448.230  < 2e-16 ***
# # CONDITIONALS                          4.251e-03  1.331e-03    3.193  0.00141 ** 
# 
# ### T>A
# glm_starfusion_T_A = glm(data = filter(answerals.base_substitutions, Substitution == "T>A"), count ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion_T_A)
# # Coefficients:
# #                                        Estimate Std. Error z value Pr(>|z|)    
# # (Intercept)                           7.247e+00  1.803e-02 401.926  < 2e-16 ***
# # CONDITIONALS                          1.202e-02  3.700e-03   3.247  0.00117 ** 
# 
# ### T>C
# glm_starfusion_T_C = glm(data = filter(answerals.base_substitutions, Substitution == "T>C"), count ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion_T_C)
# # Coefficients:
# #                                        Estimate Std. Error z value Pr(>|z|)    
# # (Intercept)                           9.563e+00  5.869e-03 1629.54   <2e-16 ***
# # CONDITIONALS                          1.433e-02  1.214e-03   11.81   <2e-16 ***
# 
# ### T>G
# glm_starfusion_T_G = glm(data = filter(answerals.base_substitutions, Substitution == "T>G"), count ~ CONDITION + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion_T_G)
# # Coefficients:
# #                                        Estimate Std. Error z value Pr(>|z|)    
# # (Intercept)                           7.741e+00  1.456e-02 531.774   <2e-16 ***
# # CONDITIONALS                          2.634e-03  2.995e-03   0.879    0.379    


ipsc_mn_als_datasets.Substitution.stat <- answerals.base_substitutions %>% group_by(Substitution) %>% t_test(count ~ CONDITION) %>% mutate(p.signif = c("**","ns","**","**","****","ns"), y.position = c(4500,5500,20500,2700,25000,3900)) 
# ipsc_mn_als_datasets.Substitution.stat
# # Substitution .y.   group1 group2    n1    n2 statistic    df      p p.signif y.position
# #   <fct>        <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>  <dbl> <chr>         <dbl>
# # 1 C>A          count CTRL   ALS       42   238     -2.14  59.5 0.0362 **             4500
# # 2 C>G          count CTRL   ALS       42   238     -1.77  62.4 0.0812 ns             5500
# # 3 C>T          count CTRL   ALS       42   238     -1.86  63.6 0.0672 **            20500
# # 4 T>A          count CTRL   ALS       42   238     -2.27  56.2 0.0269 **             2700
# # 5 T>C          count CTRL   ALS       42   238     -2.23  56.0 0.0294 ****          25000
# # 6 T>G          count CTRL   ALS       42   238     -1.86  63.8 0.0674 ns             3900
answerals.base_substitutions.violin <- ggplot(answerals.base_substitutions, aes(x = CONDITION, y = count, colour = CONDITION)) +
    ggrastr::rasterise(geom_violin(adjust = 1), dpi = 72) +
    ggrastr::rasterise(geom_sina(size = 0.5), dpi = 72) +
    ggrastr::rasterise(geom_boxplot(aes(fill = CONDITION), colour = "black", width=0.2, alpha = 0.5, outlier.shape = NA), dpi = 72) + 
    scale_colour_manual(values = c("dodgerblue2", "firebrick2")) + scale_fill_manual(values = c("dodgerblue2", "firebrick2")) +
    theme_oz() + theme(legend.position = "none", plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5), panel.border = element_blank(), axis.ticks = element_line(colour = "black") ) +
    facet_wrap(~ Substitution, scales = "free", nrow = 2)  + labs(y = "Number of Variants", x = "") + #, title = "Base substitution types"
    stat_pvalue_manual(ipsc_mn_als_datasets.Substitution.stat, label = "p.signif", tip.length = 0.01, size = 4, hide.ns = TRUE)
answerals.base_substitutions.violin

Fusions

Venn overlap unqiue gene fusions

ipsc_mn_als_datasets.starfusion.als_events <- ipsc_mn_als_datasets.starfusion %>% filter(condition == "als") %>% distinct(fusion_name) %>% pull(fusion_name)
ipsc_mn_als_datasets.starfusion.ctrl_events <- ipsc_mn_als_datasets.starfusion %>% filter(condition == "ctrl") %>% distinct(fusion_name) %>% pull(fusion_name)
ipsc_mn_als_datasets.starfusion.als_ctrl_events <- list("ALS" = ipsc_mn_als_datasets.starfusion.als_events, "CTRL" = ipsc_mn_als_datasets.starfusion.ctrl_events)
ipsc_mn_als_datasets.starfusion.als_ctrl_events_venn <- ggvenn(ipsc_mn_als_datasets.starfusion.als_ctrl_events, fill_color = c("firebrick2", "dodgerblue2"), set_name_color = c("firebrick2", "dodgerblue2"), show_percentage = FALSE, stroke_size = 0.4, set_name_size = 3.5) + labs(subtitle = "Unique Gene Fusions")  + 
  theme(plot.subtitle = element_text(hjust = 0.5, size = 9), plot.margin = unit(c(0, -1, 0, -1), "lines"))
ipsc_mn_als_datasets.starfusion.als_ctrl_events_venn

Violin ALS vs CTRL

ipsc_mn_als_datasets.starfusion.n = ipsc_mn_als_datasets.starfusion %>% 
  add_count(dataset_sample) %>% # count number of fusions per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, n, CONDITION, dataset, total_reads) #keep 1 row per sample

# add samples with 0 fusions
ipsc_mn_als_datasets.starfusion.samples_no_fusions = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% ipsc_mn_als_datasets.starfusion.n$dataset_sample] # "smith_tardbp_1" "smith_ctrl_1"  0 events
ipsc_mn_als_datasets.paired_end.metadata.no_fusions = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% ipsc_mn_als_datasets.starfusion.samples_no_fusions) %>% 
  left_join(multiqc_rseqc_read_distribution.tsv) %>%
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), n = 0) %>% select(dataset_sample, n, CONDITION, dataset, total_reads)
ipsc_mn_als_datasets.starfusion.n = bind_rows(ipsc_mn_als_datasets.starfusion.n, ipsc_mn_als_datasets.paired_end.metadata.no_fusions)

glm_starfusion = glm(data = ipsc_mn_als_datasets.starfusion.n, n ~ CONDITION + dataset + rms::rcs(total_reads, 3), family=poisson)
summary(glm_starfusion)

## 
## Call:
## glm(formula = n ~ CONDITION + dataset + rms::rcs(total_reads, 
##     3), family = poisson, data = ipsc_mn_als_datasets.starfusion.n)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.7724  -1.0338  -0.2193   0.6234   5.8605  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           2.121e+00  3.330e-01   6.369 1.90e-10 ***
## CONDITIONALS                          1.327e-01  4.282e-02   3.098  0.00195 ** 
## datasetcatanese                      -2.166e-01  1.355e-01  -1.599  0.10981    
## datasetdafinca.c9orf72               -1.340e+00  2.147e-01  -6.243 4.30e-10 ***
## datasetdafinca.tardbp                -4.989e-01  2.798e-01  -1.783  0.07455 .  
## datasetdesantis                      -5.775e-01  2.751e-01  -2.099  0.03584 *  
## datasetkiskinis                       7.013e-01  2.969e-01   2.362  0.01816 *  
## datasetneurolincs.diMN               -4.582e-01  6.999e-02  -6.546 5.90e-11 ***
## datasetneurolincs.iMN                -8.916e-01  1.271e-01  -7.016 2.28e-12 ***
## datasetsmith                         -2.752e+00  6.484e-01  -4.244 2.20e-05 ***
## datasetsommer                        -1.106e+00  1.971e-01  -5.614 1.98e-08 ***
## datasetwang                           3.354e-01  2.454e-01   1.367  0.17173    
## rms::rcs(total_reads, 3)total_reads   5.115e-09  4.914e-09   1.041  0.29785    
## rms::rcs(total_reads, 3)total_reads' -7.908e-09  3.231e-09  -2.447  0.01439 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1202.77  on 395  degrees of freedom
## Residual deviance:  747.06  on 382  degrees of freedom
## AIC: 2356.8
## 
## Number of Fisher Scoring iterations: 4

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           2.121e+00  3.330e-01   6.369 1.90e-10 ***
# CONDITIONALS                          1.327e-01  4.282e-02   3.098  0.00195 ** 

ipsc_mn_als_datasets.starfusion.n.stats = tibble(group1 = "CTRL", group2 = "ALS", p.signif = "**", y.position = 40, xmin = 1, xmax = 2)
ipsc_mn_als_datasets.starfusion.violin <- violin_plot(ipsc_mn_als_datasets.starfusion.n, color_by = "CONDITION", continuous = "n", cols = c("dodgerblue2", "firebrick2"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(0,42), dpi = 72, ylabel = "Number of Fusion Events", title = "All Fusion types") +
  stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.n.stats, tip.length = 0.01, size = 4)
ipsc_mn_als_datasets.starfusion.violin

Table S8 Variants per sample

answerals.vcfannotate.s8 = answerals.vcfannotate %>% select(dataset, sample, n, variant_type, condition, mutation, Total = total) %>% 
  pivot_wider(names_from = "variant_type", values_from = "n", values_fill = 0) %>% 
  select(dataset, sample, condition, mutation, Total, SNP, Insertion, Deletion) %>% #, Complex
  arrange(condition)
kbl(answerals.vcfannotate.s8) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), fixed_thead = T) %>% scroll_box(width = "100%", height = "500px")

dataset	sample	condition	mutation	Total	SNP	Insertion	Deletion
answerals	CASE_NEUDE902GCT	als	c9orf72	56617	48220	4710	3675
answerals	CASE_NEUAA599TMX	als	sporadic	56584	48981	3870	3725
answerals	CASE_NEUAB000NKC	als	c9orf72	56355	48388	4636	3326
answerals	CASE_NEUAE228FF6	als	sporadic	56503	48530	4342	3623
answerals	CASE_NEUAE993EPR	als	sporadic	70176	60471	5233	4453
answerals	CASE_NEUAF553MJ3	als	sporadic	57107	49228	4267	3603
answerals	CASE_NEUAG241NUD	als	sporadic	57675	49382	4400	3885
answerals	CASE_NEUAG603XLK	als	pfn1	47174	40535	3639	2994
answerals	CASE_NEUAG766ULB	als	sporadic	60427	51744	4934	3742
answerals	CASE_NEUAL076FCE	als	sporadic	59793	50869	4989	3924
answerals	CASE_NEUAM655HF7	als	sporadic	55081	47208	4230	3634
answerals	CASE_NEUAP285GGU	als	sporadic	56688	48716	4429	3537
answerals	CASE_NEUAT234RK6	als	sporadic	57608	49123	4986	3488
answerals	CASE_NEUAW157NMJ	als	sporadic	58189	50092	4436	3653
answerals	CASE_NEUAW717TN6	als	sporadic	57854	49562	4370	3910
answerals	CASE_NEUAX665ZHY	als	sporadic	57161	48894	4469	3789
answerals	CASE_NEUAY067UTB	als	chchd1	60322	51828	4567	3919
answerals	CASE_NEUAZ394JEZ	als	sporadic	62141	53706	4534	3890
answerals	CASE_NEUBA169GXD	als	sporadic	60349	51652	4797	3890
answerals	CASE_NEUBC901KL3	als	sporadic	60956	51771	5372	3807
answerals	CASE_NEUBC998WWB	als	sqstm1	59342	50924	4598	3813
answerals	CASE_NEUBD218YR3	als	fig4	56370	48129	4615	3614
answerals	CASE_NEUBD288RXQ	als	sporadic	55161	47418	4097	3639
answerals	CASE_NEUBK117YXL	als	sporadic	59463	51145	4665	3643
answerals	CASE_NEUBM949LA5	als	sporadic	58583	50142	4614	3812
answerals	CASE_NEUBN979ZZ5	als	sporadic	61495	52837	4928	3722
answerals	CASE_NEUBT273FH3	als	sporadic	56265	48124	4487	3646
answerals	CASE_NEUBW008RJ5	als	sporadic	61206	52495	4727	3979
answerals	CASE_NEUBY734PFR	als	sporadic	55299	47536	4173	3585
answerals	CASE_NEUCB613CA9	als	sporadic	58135	49890	4495	3741
answerals	CASE_NEUCD502BFU	als	sod1	60442	51808	4904	3719
answerals	CASE_NEUCE965ZGK	als	c9orf72	57625	49386	4460	3771
answerals	CASE_NEUCF538BRM	als	sporadic	58487	49990	4721	3769
answerals	CASE_NEUCG511MJ6	als	sporadic	61314	52448	5018	3828
answerals	CASE_NEUCN596RR3	als	sporadic	57637	49881	4177	3570
answerals	CASE_NEUCP218XVV	als	sporadic	62812	54049	4920	3829
answerals	CASE_NEUCT022VTK	als	sporadic	60493	51793	4646	4042
answerals	CASE_NEUCT842RJV	als	sod1	58214	49653	4894	3657
answerals	CASE_NEUCU076ADN	als	sporadic	61936	53586	4432	3905
answerals	CASE_NEUCU245GBQ	als	sporadic	55305	47118	4599	3578
answerals	CASE_NEUCV578DFJ	als	sporadic	56700	48860	4347	3484
answerals	CASE_NEUDD018KAH	als	sporadic	55104	47304	4203	3584
answerals	CASE_NEUDG000ZG5	als	sporadic	57075	49593	3882	3597
answerals	CASE_NEUDG272XWC	als	sporadic	57736	49728	4263	3738
answerals	CASE_NEUDH063DEA	als	sporadic	56071	47885	4555	3621
answerals	CASE_NEUDM949BAR	als	sporadic	69775	60555	4968	4236
answerals	CASE_NEUDP155HFH	als	sporadic	60357	51432	4971	3944
answerals	CASE_NEUDT709YHN	als	c9orf72	56328	48912	3862	3550
answerals	CASE_NEUDY534KGP	als	sporadic	60624	51916	4824	3870
answerals	CASE_NEUDZ473EGM	als	c9orf72	61242	52126	5334	3775
answerals	CASE_NEUEB422WW0	als	sporadic	60128	51463	4794	3861
answerals	CASE_NEUEC400NYR	als	setx	59892	51429	4475	3977
answerals	CASE_NEUEK191WYC	als	sporadic	57379	49199	4465	3707
answerals	CASE_NEUEM029XXZ	als	sporadic	56477	48694	4220	3552
answerals	CASE_NEUEM720BUU	als	c9orf72	54756	46712	4925	3114
answerals	CASE_NEUEN017PCJ	als	sporadic	59090	50634	4675	3777
answerals	CASE_NEUEN476CLW	als	c9orf72	57817	49656	4548	3610
answerals	CASE_NEUET072VDG	als	sporadic	60255	51758	4671	3822
answerals	CASE_NEUEU318NY2	als	sporadic	59240	50849	4534	3848
answerals	CASE_NEUEU558MNK	als	sporadic	57011	48900	4349	3756
answerals	CASE_NEUEY478NZP	als	sod1	60025	51188	4967	3857
answerals	CASE_NEUFB989DT1	als	sporadic	57424	49535	4190	3690
answerals	CASE_NEUFH122WN7	als	sqstm1	60603	52149	4532	3907
answerals	CASE_NEUFH461AAN	als	sporadic	68843	59569	4938	4322
answerals	CASE_NEUFU395MJD	als	sporadic	56093	48085	4401	3600
answerals	CASE_NEUFV237VCZ	als	c9orf72	56842	48626	4510	3700
answerals	CASE_NEUFX386KMB	als	sporadic	58198	49094	5283	3813
answerals	CASE_NEUFY342UNG	als	sporadic	58116	50159	4073	3874
answerals	CASE_NEUGD965XVD	als	sporadic	60862	52133	4767	3955
answerals	CASE_NEUGE540TC4	als	sporadic	53921	46403	4014	3495
answerals	CASE_NEUGH995TFK	als	c9orf72	61513	52988	4649	3868
answerals	CASE_NEUGJ081HKR	als	c9orf72	55208	47517	4172	3514
answerals	CASE_NEUGL543NJ1	als	sporadic	57839	49612	4487	3733
answerals	CASE_NEUGP781PDU	als	sporadic	56686	48624	4368	3685
answerals	CASE_NEUGR121TFD	als	sporadic	69750	60168	5084	4486
answerals	CASE_NEUGR539MVT	als	sporadic	58504	50015	4595	3881
answerals	CASE_NEUGW326BRV	als	sporadic	52833	45536	4027	3265
answerals	CASE_NEUGW340YEB	als	sporadic	60711	52631	4259	3810
answerals	CASE_NEUHB491NGF	als	sporadic	58510	50251	4430	3820
answerals	CASE_NEUHG644RYB	als	sporadic	59879	51759	4296	3816
answerals	CASE_NEUHG791RV5	als	sporadic	56657	48931	4109	3614
answerals	CASE_NEUHK991AVP	als	sod1	57556	49162	4595	3790
answerals	CASE_NEUHL999WWT	als	sporadic	57573	49110	4678	3779
answerals	CASE_NEUHM532NDD	als	sporadic	60699	52285	4581	3828
answerals	CASE_NEUHT569HT1	als	sporadic	62072	53367	4747	3951
answerals	CASE_NEUHV216EHA	als	sporadic	57938	49858	4351	3717
answerals	CASE_NEUHW530WD8	als	sporadic	60842	52228	4727	3878
answerals	CASE_NEUHW590KBH	als	sporadic	54091	46700	4011	3373
answerals	CASE_NEUHY206ZEQ	als	sporadic	55933	47862	4431	3626
answerals	CASE_NEUHZ302VU4	als	sporadic	53741	46046	4170	3519
answerals	CASE_NEUJA217MTJ	als	sporadic	59038	51588	3935	3508
answerals	CASE_NEUJA666PYD	als	sporadic	57285	49098	4458	3718
answerals	CASE_NEUJC191RLE	als	pfn1	57936	49894	4339	3693
answerals	CASE_NEUJG311WGV	als	sporadic	57839	49407	4710	3712
answerals	CASE_NEUJG885PY7	als	sporadic	60245	51768	4580	3889
answerals	CASE_NEUJH197AK2	als	sporadic	58104	49709	4666	3720
answerals	CASE_NEUJK720JCL	als	sporadic	63459	53214	6353	3879
answerals	CASE_NEUJL547WVQ	als	sporadic	66479	58462	3618	4387
answerals	CASE_NEUJL595JAZ	als	sporadic	68444	59292	4865	4278
answerals	CASE_NEUJP935AVF	als	sporadic	58826	50443	4557	3815
answerals	CASE_NEUJX780XXK	als	sporadic	58759	50213	4626	3913
answerals	CASE_NEUJX990GR5	als	c9orf72	62532	53432	5134	3957
answerals	CASE_NEUJY426MBU	als	sporadic	63184	53940	5247	3990
answerals	CASE_NEUJY536DKF	als	sporadic	58841	50172	4803	3859
answerals	CASE_NEUKA860NUG	als	sporadic	57475	49594	4413	3465
answerals	CASE_NEUKD025JPF	als	sporadic	53032	45368	4160	3495
answerals	CASE_NEUKP160HX8	als	sporadic	60955	52292	4709	3940
answerals	CASE_NEUKR376CW3	als	sporadic	59716	51083	4859	3767
answerals	CASE_NEUKV547CFA	als	sporadic	58332	50402	4378	3545
answerals	CASE_NEUKY704JVA	als	sporadic	57919	49881	4214	3817
answerals	CASE_NEULA694DDC	als	sporadic	56553	48081	4925	3538
answerals	CASE_NEULA777DBT	als	sporadic	60828	52252	4780	3788
answerals	CASE_NEULD354RZB	als	sporadic	58571	50309	4500	3756
answerals	CASE_NEULH729AU2	als	sporadic	56790	48811	4431	3540
answerals	CASE_NEULL588ALG	als	sporadic	58040	50064	4307	3660
answerals	CASE_NEULL648LJ1	als	sporadic	59952	51253	4757	3931
answerals	CASE_NEULP998KDJ	als	sporadic	55783	48072	3973	3727
answerals	CASE_NEULT851ENP	als	sporadic	57842	49090	4970	3777
answerals	CASE_NEULY177TTN	als	sporadic	54390	46686	4203	3497
answerals	CASE_NEULY328CRJ	als	sporadic	57720	49451	4533	3729
answerals	CASE_NEULZ548ZXV	als	sporadic	68624	59417	4897	4298
answerals	CASE_NEUMB242CLN	als	sporadic	64129	55161	5047	3916
answerals	CASE_NEUME498PCJ	als	sporadic	58309	50715	3999	3585
answerals	CASE_NEUMH634MKT	als	sporadic	60658	51952	4866	3832
answerals	CASE_NEUMN012EVP	als	sporadic	58561	50094	4680	3778
answerals	CASE_NEUMW598YMT	als	sporadic	62333	53419	5026	3878
answerals	CASE_NEUMY871DGF	als	sporadic	60697	51759	5020	3907
answerals	CASE_NEUNA248WXL	als	sporadic	55051	47376	4045	3621
answerals	CASE_NEUNG326MFP	als	sporadic	61248	52461	4840	3937
answerals	CASE_NEUNJ155KYL	als	sporadic	60514	51907	4617	3984
answerals	CASE_NEUNJ938DUL	als	sporadic	59969	51779	4561	3625
answerals	CASE_NEUNL303HLF	als	sporadic	56991	48903	4274	3802
answerals	CASE_NEUNL415AFW	als	c9orf72	57236	49163	4505	3561
answerals	CASE_NEUNR020KV6	als	sporadic	57657	49284	4569	3797
answerals	CASE_NEUNZ171UYF	als	sporadic	58352	49773	4750	3822
answerals	CASE_NEUPG593UTV	als	sporadic	60219	51623	4928	3658
answerals	CASE_NEUPK546ZLD	als	c9orf72	58822	50244	4762	3809
answerals	CASE_NEUPM937TMY	als	sporadic	56356	48263	4429	3656
answerals	CASE_NEUPN525XEW	als	sporadic	56269	48342	4398	3523
answerals	CASE_NEUPR357AUF	als	sporadic	60436	51732	4868	3825
answerals	CASE_NEUPR600MBU	als	sporadic	55080	47217	4373	3486
answerals	CASE_NEUPY050ANK	als	sporadic	59160	50401	5042	3705
answerals	CASE_NEURA639MK8	als	sporadic	53531	46259	3799	3464
answerals	CASE_NEURF720KHA	als	sporadic	57317	49163	4440	3708
answerals	CASE_NEURJ362MXH	als	setx	57101	49137	4246	3708
answerals	CASE_NEURR881FKY	als	sporadic	56549	48837	4135	3570
answerals	CASE_NEURX315DYH	als	sporadic	57026	48201	5162	3652
answerals	CASE_NEURX909UL6	als	sporadic	57249	48874	4767	3599
answerals	CASE_NEUTA057AF6	als	sod1	63145	54036	5095	3999
answerals	CASE_NEUTA689LN5	als	sporadic	57830	49621	4555	3644
answerals	CASE_NEUTB230DA3	als	sporadic	68334	58838	5088	4393
answerals	CASE_NEUTC791PVE	als	sporadic	64212	55029	5293	3881
answerals	CASE_NEUTD314VFT	als	sporadic	58647	50159	4656	3823
answerals	CASE_NEUTD713DE3	als	sporadic	56983	48958	4482	3534
answerals	CASE_NEUTD866EAH	als	sporadic	70740	60910	5440	4375
answerals	CASE_NEUTJ613AH9	als	sporadic	53539	45913	4221	3399
answerals	CASE_NEUTL257PNR	als	sporadic	57838	49524	4614	3687
answerals	CASE_NEUTL699WJ0	als	sporadic	60387	51630	4950	3799
answerals	CASE_NEUTM934BPY	als	sporadic	60947	51341	5935	3661
answerals	CASE_NEUTN952DDG	als	c9orf72	56066	48151	4328	3580
answerals	CASE_NEUTU360YJY	als	sporadic	59112	50447	4749	3908
answerals	CASE_NEUUC458RFT	als	c9orf72	61553	53233	4547	3765
answerals	CASE_NEUUD158CJF	als	sporadic	59508	50942	4824	3730
answerals	CASE_NEUUE852BB1	als	sporadic	56400	48072	4698	3619
answerals	CASE_NEUUF613PHL	als	sporadic	55668	47492	4621	3545
answerals	CASE_NEUUH658EYH	als	sporadic	67044	57942	4822	4271
answerals	CASE_NEUUJ507EAB	als	sporadic	58926	51621	3506	3788
answerals	CASE_NEUUL256UC9	als	c9orf72	58166	50041	4368	3743
answerals	CASE_NEUUL292XRC	als	sporadic	59515	51097	4721	3686
answerals	CASE_NEUUL311NRQ	als	sporadic	57975	49431	4783	3749
answerals	CASE_NEUUP280KXL	als	sporadic	58169	49874	4518	3770
answerals	CASE_NEUUU506WZF	als	sporadic	59501	50857	4906	3733
answerals	CASE_NEUUZ216XY8	als	sporadic	61825	52520	5471	3828
answerals	CASE_NEUVD687XD8	als	sporadic	63026	54907	4313	3796
answerals	CASE_NEUVF888UHM	als	sporadic	54658	47080	4082	3494
answerals	CASE_NEUVJ560JGZ	als	sporadic	60403	51758	4810	3826
answerals	CASE_NEUVM674HUA	als	sporadic	56991	49038	4448	3496
answerals	CASE_NEUVN746WKV	als	sporadic	58209	50322	4279	3598
answerals	CASE_NEUVP060WFG	als	sporadic	58717	50229	4679	3797
answerals	CASE_NEUVR636VTP	als	sporadic	59011	50635	4539	3834
answerals	CASE_NEUVR814YCY	als	sporadic	60715	52433	4462	3810
answerals	CASE_NEUVU735HU6	als	sporadic	69147	60054	4847	4231
answerals	CASE_NEUVV225XKB	als	sporadic	59317	50646	4759	3901
answerals	CASE_NEUVW680LPK	als	c9orf72	57874	49510	4588	3767
answerals	CASE_NEUVW999FP9	als	sporadic	59423	51099	4516	3797
answerals	CASE_NEUVX902YNL	als	c9orf72	54908	46768	4417	3720
answerals	CASE_NEUWD538KT3	als	c9orf72	56245	48044	4604	3589
answerals	CASE_NEUWD946RDA	als	sporadic	62506	53415	5157	3926
answerals	CASE_NEUWH380YHV	als	sporadic	59186	50751	4635	3790
answerals	CASE_NEUWH955FJF	als	sporadic	61047	52562	4604	3873
answerals	CASE_NEUWJ389FC7	als	sporadic	57396	49162	4466	3759
answerals	CASE_NEUWM344ZLM	als	sod1	55629	47637	4284	3703
answerals	CASE_NEUWP426NBR	als	sporadic	57165	49264	4286	3605
answerals	CASE_NEUWX167DAH	als	sporadic	58440	50098	4519	3815
answerals	CASE_NEUWY079WUH	als	sporadic	66375	56843	5286	4234
answerals	CASE_NEUWZ614ARQ	als	sporadic	54933	46815	4446	3664
answerals	CASE_NEUXD985ZU1	als	sporadic	59705	51105	4765	3824
answerals	CASE_NEUXE491RMM	als	sporadic	55908	48343	3994	3565
answerals	CASE_NEUXG265ME9	als	sporadic	58728	50395	4414	3905
answerals	CASE_NEUXH833LL4	als	sporadic	59041	50605	4621	3805
answerals	CASE_NEUXP289KRC	als	sod1	57123	48766	4667	3684
answerals	CASE_NEUXP495XE2	als	sporadic	59608	51399	4358	3842
answerals	CASE_NEUXP595FFP	als	sporadic	53531	45773	4199	3551
answerals	CASE_NEUXV122FN3	als	sporadic	61048	52398	4683	3955
answerals	CASE_NEUXX361RUG	als	sporadic	55793	48069	4188	3530
answerals	CASE_NEUYC055XPZ	als	sporadic	57919	49334	4796	3776
answerals	CASE_NEUYC303AJY	als	sporadic	58961	50283	4820	3846
answerals	CASE_NEUYD306HAB	als	sporadic	58116	49543	5005	3556
answerals	CASE_NEUYG208KVV	als	sporadic	56507	48381	4375	3744
answerals	CASE_NEUYG298CA9	als	sporadic	60406	51438	5148	3813
answerals	CASE_NEUYH924UCE	als	sporadic	60296	51515	4748	4023
answerals	CASE_NEUYJ705VN9	als	sporadic	60720	52113	4782	3815
answerals	CASE_NEUYK029WUU	als	sporadic	55198	47101	4462	3624
answerals	CASE_NEUYL008GM4	als	sporadic	57699	49865	4109	3717
answerals	CASE_NEUYL149PRF	als	sporadic	56637	48855	4120	3655
answerals	CASE_NEUYP226CPW	als	sporadic	61950	53333	4720	3886
answerals	CASE_NEUYP235ZLD	als	tardbp	56820	48881	4254	3675
answerals	CASE_NEUYT193FFG	als	sporadic	58074	49545	4676	3843
answerals	CASE_NEUYY225MNZ	als	c9orf72	53495	46046	3923	3522
answerals	CASE_NEUYY614DN8	als	sporadic	62481	53489	5170	3811
answerals	CASE_NEUYY878JGP	als	sporadic	60999	52371	4890	3730
answerals	CASE_NEUZD231YZ6	als	sporadic	54797	46712	4520	3555
answerals	CASE_NEUZE432DZM	als	sporadic	61125	52221	5070	3824
answerals	CASE_NEUZF321EW4	als	sod1	57596	49433	4445	3714
answerals	CASE_NEUZF473UEP	als	sporadic	64617	55405	5175	4030
answerals	CASE_NEUZJ053JGZ	als	sporadic	59197	50800	4539	3853
answerals	CASE_NEUZK054DP5	als	sporadic	58652	50511	4362	3769
answerals	CASE_NEUZN836GME	als	sporadic	60499	51754	4899	3836
answerals	CASE_NEUZN936HJ9	als	sporadic	55162	47141	4250	3767
answerals	CASE_NEUZP278MR4	als	sporadic	58249	49800	4552	3889
answerals	CASE_NEUZT557DHF	als	sporadic	59667	51310	4558	3792
answerals	CASE_NEUZT902WVB	als	sporadic	63471	54337	4965	4155
answerals	CASE_NEUZV656DD1	als	sporadic	69727	60051	5192	4473
answerals	CASE_NEUZW701NNF	als	c9orf72	54607	46701	4364	3535
answerals	CASE_NEUZX521TKK	als	sporadic	56578	48274	4694	3604
answerals	CASE_NEUZX847VWV	als	sporadic	58281	49775	4672	3831
answerals	CASE_NEUZY128BJ2	als	sporadic	63551	54316	5192	4036
answerals	CASE_NEUZY975XKL	als	sporadic	57443	49428	4230	3778
answerals	CTRL_NEUAA485DZL	ctrl	ctrl	63238	54544	4780	3909
answerals	CTRL_NEUAJ025JC3	ctrl	ctrl	56132	48574	4080	3466
answerals	CTRL_NEUAJ928PAA	ctrl	ctrl	58615	50164	4733	3710
answerals	CTRL_NEUCA748GF2	ctrl	ctrl	57039	48969	4365	3699
answerals	CTRL_NEUCV136DHM	ctrl	ctrl	60751	51891	4924	3924
answerals	CTRL_NEUCV809LL4	ctrl	ctrl	53289	45860	4098	3323
answerals	CTRL_NEUDA782GW3	ctrl	ctrl	57198	49513	4260	3415
answerals	CTRL_NEUDE949BP3	ctrl	ctrl	57359	49426	4398	3529
answerals	CTRL_NEUDM126GNG	ctrl	ctrl	59261	50526	4954	3766
answerals	CTRL_NEUDT762KUL	ctrl	ctrl	55473	47767	4227	3468
answerals	CTRL_NEUEB210XRC	ctrl	ctrl	57704	49296	4689	3707
answerals	CTRL_NEUEY565NWT	ctrl	ctrl	56602	48391	4712	3489
answerals	CTRL_NEUFL733GX5	ctrl	ctrl	64451	54811	5716	3914
answerals	CTRL_NEUFZ500KDB	ctrl	ctrl	57073	48888	4595	3581
answerals	CTRL_NEUFZ508VBV	ctrl	ctrl	53898	46830	3735	3326
answerals	CTRL_NEUHE723FGT	ctrl	ctrl	58113	50153	4255	3694
answerals	CTRL_NEUHZ716BZ2	ctrl	ctrl	54109	46540	4156	3404
answerals	CTRL_NEUJH290RH7	ctrl	ctrl	53571	46469	3731	3367
answerals	CTRL_NEUJX341NDP	ctrl	ctrl	53093	45615	4041	3427
answerals	CTRL_NEUKW131XJ2	ctrl	ctrl	55174	47339	4338	3490
answerals	CTRL_NEULL933JXY	ctrl	ctrl	57664	49898	4032	3723
answerals	CTRL_NEUMA002VLD	ctrl	ctrl	59949	51244	4686	4011
answerals	CTRL_NEUMF089KLV	ctrl	ctrl	56930	48697	4475	3751
answerals	CTRL_NEUML507PFJ	ctrl	ctrl	55269	47121	4524	3616
answerals	CTRL_NEUMN061ATZ	ctrl	ctrl	53189	45465	4159	3552
answerals	CTRL_NEUMT184NWC	ctrl	ctrl	60108	51580	4731	3791
answerals	CTRL_NEUNC876ZB2	ctrl	ctrl	61491	52926	4499	4057
answerals	CTRL_NEUNN472ACB	ctrl	ctrl	69191	59326	5702	4149
answerals	CTRL_NEUNW343RXP	ctrl	ctrl	55652	47504	4485	3657
answerals	CTRL_NEUPH301NNX	ctrl	ctrl	54521	47158	3958	3397
answerals	CTRL_NEUPL878MTL	ctrl	ctrl	58345	50015	4518	3804
answerals	CTRL_NEUPW536ZKZ	ctrl	ctrl	58923	50330	4811	3771
answerals	CTRL_NEURJ861MMD	ctrl	ctrl	58755	51011	4136	3605
answerals	CTRL_NEURV546WMW	ctrl	ctrl	61817	53187	4606	4015
answerals	CTRL_NEUUV825HYF	ctrl	ctrl	54032	46512	4121	3389
answerals	CTRL_NEUVZ050YX7	ctrl	ctrl	54408	46947	4018	3437
answerals	CTRL_NEUWN092BVG	ctrl	ctrl	56657	48926	3971	3752
answerals	CTRL_NEUWT164JRQ	ctrl	ctrl	58778	50113	4861	3794
answerals	CTRL_NEUXC258VTR	ctrl	ctrl	61355	52769	4716	3859
answerals	CTRL_NEUXP955XW7	ctrl	ctrl	58954	50522	4798	3628
answerals	CTRL_NEUXW311EFC	ctrl	ctrl	53050	45720	3940	3384
answerals	CTRL_NEUYM205MRL	ctrl	ctrl	58659	50854	4064	3734

Table S9 Gene fusions per sample

ipsc_mn_als_datasets.starfusion.n = ipsc_mn_als_datasets.starfusion %>% 
  add_count(dataset_sample) %>% # count number of fusions per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset, sample, condition, fusion_number = n, total_reads, dataset_sample, mutation) #keep 1 row per sample
 # add samples with 0 fusions
ipsc_mn_als_datasets.starfusion.samples_no_fusions = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% ipsc_mn_als_datasets.starfusion.n$dataset_sample] 
ipsc_mn_als_datasets.paired_end.metadata.no_fusions = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% ipsc_mn_als_datasets.starfusion.samples_no_fusions) %>% 
  left_join(multiqc_rseqc_read_distribution.tsv) %>%
  mutate(fusion_number = 0) %>% select(dataset_sample, sample, fusion_number, condition, dataset, total_reads, mutation)
ipsc_mn_als_datasets.starfusion.n = bind_rows(ipsc_mn_als_datasets.starfusion.n, ipsc_mn_als_datasets.paired_end.metadata.no_fusions)
ipsc_mn_als_datasets.starfusion.table_s9 = ipsc_mn_als_datasets.starfusion.n %>% select(dataset, sample, condition, mutation, fusion_number, total_reads)
kbl(ipsc_mn_als_datasets.starfusion.table_s9) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), fixed_thead = T) %>% scroll_box(width = "100%", height = "500px")

dataset	sample	condition	mutation	fusion_number	total_reads
answerals	CASE_NEUDE902GCT	als	c9orf72	13	80351595
answerals	CTRL_NEUAA485DZL	ctrl	ctrl	6	92629631
answerals	CASE_NEUAA599TMX	als	sporadic	6	77223828
answerals	CASE_NEUAB000NKC	als	c9orf72	31	71106777
answerals	CASE_NEUAE228FF6	als	sporadic	2	63533855
answerals	CASE_NEUAE993EPR	als	sporadic	9	93223837
answerals	CASE_NEUAF553MJ3	als	sporadic	8	92294028
answerals	CASE_NEUAG241NUD	als	sporadic	7	88487027
answerals	CASE_NEUAG603XLK	als	pfn1	11	41359233
answerals	CASE_NEUAG766ULB	als	sporadic	12	85338776
answerals	CTRL_NEUAJ025JC3	ctrl	ctrl	15	78856459
answerals	CTRL_NEUAJ928PAA	ctrl	ctrl	16	96697354
answerals	CASE_NEUAL076FCE	als	sporadic	12	88517839
answerals	CASE_NEUAM655HF7	als	sporadic	12	70099172
answerals	CASE_NEUAP285GGU	als	sporadic	15	71653943
answerals	CASE_NEUAT234RK6	als	sporadic	14	86690836
answerals	CASE_NEUAW157NMJ	als	sporadic	25	94641466
answerals	CASE_NEUAW717TN6	als	sporadic	8	96444518
answerals	CASE_NEUAX665ZHY	als	sporadic	11	96375698
answerals	CASE_NEUAY067UTB	als	chchd1	14	103940017
answerals	CASE_NEUAZ394JEZ	als	sporadic	7	90298769
answerals	CASE_NEUBA169GXD	als	sporadic	13	90044591
answerals	CASE_NEUBC901KL3	als	sporadic	11	96083124
answerals	CASE_NEUBC998WWB	als	sqstm1	11	97383223
answerals	CASE_NEUBD218YR3	als	fig4	19	72915834
answerals	CASE_NEUBD288RXQ	als	sporadic	10	92088313
answerals	CASE_NEUBK117YXL	als	sporadic	21	92338200
answerals	CASE_NEUBM949LA5	als	sporadic	9	86250060
answerals	CASE_NEUBN979ZZ5	als	sporadic	4	89827770
answerals	CASE_NEUBT273FH3	als	sporadic	17	74542618
answerals	CASE_NEUBW008RJ5	als	sporadic	14	106645947
answerals	CASE_NEUBY734PFR	als	sporadic	6	74456844
answerals	CTRL_NEUCA748GF2	ctrl	ctrl	8	82008988
answerals	CASE_NEUCB613CA9	als	sporadic	9	90541534
answerals	CASE_NEUCD502BFU	als	sod1	9	89586112
answerals	CASE_NEUCE965ZGK	als	c9orf72	20	78627760
answerals	CASE_NEUCF538BRM	als	sporadic	7	97574144
answerals	CASE_NEUCG511MJ6	als	sporadic	6	98813421
answerals	CASE_NEUCN596RR3	als	sporadic	5	89512093
answerals	CASE_NEUCP218XVV	als	sporadic	12	93789222
answerals	CASE_NEUCT022VTK	als	sporadic	11	100874817
answerals	CASE_NEUCT842RJV	als	sod1	26	86138539
answerals	CASE_NEUCU076ADN	als	sporadic	6	95677719
answerals	CASE_NEUCU245GBQ	als	sporadic	12	72116830
answerals	CTRL_NEUCV136DHM	ctrl	ctrl	10	96543719
answerals	CASE_NEUCV578DFJ	als	sporadic	10	72026572
answerals	CTRL_NEUCV809LL4	ctrl	ctrl	19	60185905
answerals	CTRL_NEUDA782GW3	ctrl	ctrl	16	71137627
answerals	CASE_NEUDD018KAH	als	sporadic	14	70760037
answerals	CTRL_NEUDE949BP3	ctrl	ctrl	18	84067732
answerals	CASE_NEUDG000ZG5	als	sporadic	13	79910862
answerals	CASE_NEUDG272XWC	als	sporadic	10	82329332
answerals	CASE_NEUDH063DEA	als	sporadic	8	66730587
answerals	CTRL_NEUDM126GNG	ctrl	ctrl	12	89281476
answerals	CASE_NEUDM949BAR	als	sporadic	9	79446358
answerals	CASE_NEUDP155HFH	als	sporadic	8	104761280
answerals	CASE_NEUDT709YHN	als	c9orf72	6	80752554
answerals	CTRL_NEUDT762KUL	ctrl	ctrl	17	68969167
answerals	CASE_NEUDY534KGP	als	sporadic	10	91012561
answerals	CASE_NEUDZ473EGM	als	c9orf72	10	112302126
answerals	CTRL_NEUEB210XRC	ctrl	ctrl	10	83801109
answerals	CASE_NEUEB422WW0	als	sporadic	18	102960267
answerals	CASE_NEUEC400NYR	als	setx	7	92373132
answerals	CASE_NEUEK191WYC	als	sporadic	9	84425639
answerals	CASE_NEUEM029XXZ	als	sporadic	14	77394197
answerals	CASE_NEUEM720BUU	als	c9orf72	39	63755467
answerals	CASE_NEUEN017PCJ	als	sporadic	10	90774153
answerals	CASE_NEUEN476CLW	als	c9orf72	21	76588865
answerals	CASE_NEUET072VDG	als	sporadic	15	95322550
answerals	CASE_NEUEU318NY2	als	sporadic	13	91271120
answerals	CASE_NEUEU558MNK	als	sporadic	13	83207564
answerals	CASE_NEUEY478NZP	als	sod1	13	83363466
answerals	CTRL_NEUEY565NWT	ctrl	ctrl	10	82690328
answerals	CASE_NEUFB989DT1	als	sporadic	6	80810272
answerals	CASE_NEUFH122WN7	als	sqstm1	5	108588802
answerals	CASE_NEUFH461AAN	als	sporadic	20	95778777
answerals	CTRL_NEUFL733GX5	ctrl	ctrl	6	94018469
answerals	CASE_NEUFU395MJD	als	sporadic	11	72993028
answerals	CASE_NEUFV237VCZ	als	c9orf72	20	89352295
answerals	CASE_NEUFX386KMB	als	sporadic	9	96918010
answerals	CASE_NEUFY342UNG	als	sporadic	10	90963548
answerals	CTRL_NEUFZ500KDB	ctrl	ctrl	11	85044761
answerals	CTRL_NEUFZ508VBV	ctrl	ctrl	8	75909259
answerals	CASE_NEUGD965XVD	als	sporadic	5	103651614
answerals	CASE_NEUGE540TC4	als	sporadic	11	64036466
answerals	CASE_NEUGH995TFK	als	c9orf72	7	85349248
answerals	CASE_NEUGJ081HKR	als	c9orf72	8	75455646
answerals	CASE_NEUGL543NJ1	als	sporadic	9	92931442
answerals	CASE_NEUGP781PDU	als	sporadic	10	80833254
answerals	CASE_NEUGR121TFD	als	sporadic	15	102872584
answerals	CASE_NEUGR539MVT	als	sporadic	7	94175062
answerals	CASE_NEUGW326BRV	als	sporadic	18	69987454
answerals	CASE_NEUGW340YEB	als	sporadic	15	98422534
answerals	CASE_NEUHB491NGF	als	sporadic	12	105880561
answerals	CTRL_NEUHE723FGT	ctrl	ctrl	13	98431529
answerals	CASE_NEUHG644RYB	als	sporadic	9	96700132
answerals	CASE_NEUHG791RV5	als	sporadic	15	93752450
answerals	CASE_NEUHK991AVP	als	sod1	12	95449413
answerals	CASE_NEUHL999WWT	als	sporadic	11	91645541
answerals	CASE_NEUHM532NDD	als	sporadic	12	97995986
answerals	CASE_NEUHT569HT1	als	sporadic	7	97925023
answerals	CASE_NEUHV216EHA	als	sporadic	10	100671641
answerals	CASE_NEUHW530WD8	als	sporadic	16	105977870
answerals	CASE_NEUHW590KBH	als	sporadic	10	72181419
answerals	CASE_NEUHY206ZEQ	als	sporadic	18	74090361
answerals	CASE_NEUHZ302VU4	als	sporadic	13	67530789
answerals	CTRL_NEUHZ716BZ2	ctrl	ctrl	13	71207355
answerals	CASE_NEUJA217MTJ	als	sporadic	6	115185396
answerals	CASE_NEUJA666PYD	als	sporadic	19	80935095
answerals	CASE_NEUJC191RLE	als	pfn1	8	101473499
answerals	CASE_NEUJG311WGV	als	sporadic	10	90925131
answerals	CASE_NEUJG885PY7	als	sporadic	13	98116859
answerals	CASE_NEUJH197AK2	als	sporadic	20	77814469
answerals	CTRL_NEUJH290RH7	ctrl	ctrl	6	66856215
answerals	CASE_NEUJK720JCL	als	sporadic	6	106522739
answerals	CASE_NEUJL547WVQ	als	sporadic	7	86686484
answerals	CASE_NEUJL595JAZ	als	sporadic	16	85731672
answerals	CASE_NEUJP935AVF	als	sporadic	7	88750101
answerals	CTRL_NEUJX341NDP	ctrl	ctrl	7	71651755
answerals	CASE_NEUJX780XXK	als	sporadic	5	100118041
answerals	CASE_NEUJX990GR5	als	c9orf72	2	112621195
answerals	CASE_NEUJY426MBU	als	sporadic	6	122500065
answerals	CASE_NEUJY536DKF	als	sporadic	10	98537826
answerals	CASE_NEUKA860NUG	als	sporadic	15	62840884
answerals	CASE_NEUKD025JPF	als	sporadic	12	60919280
answerals	CASE_NEUKP160HX8	als	sporadic	12	102003991
answerals	CASE_NEUKR376CW3	als	sporadic	12	97538990
answerals	CASE_NEUKV547CFA	als	sporadic	12	69364009
answerals	CTRL_NEUKW131XJ2	ctrl	ctrl	17	74096752
answerals	CASE_NEUKY704JVA	als	sporadic	9	80410172
answerals	CASE_NEULA694DDC	als	sporadic	10	72567703
answerals	CASE_NEULA777DBT	als	sporadic	18	110522400
answerals	CASE_NEULD354RZB	als	sporadic	12	92499799
answerals	CASE_NEULH729AU2	als	sporadic	8	66477122
answerals	CASE_NEULL588ALG	als	sporadic	7	83847300
answerals	CASE_NEULL648LJ1	als	sporadic	14	110155878
answerals	CTRL_NEULL933JXY	ctrl	ctrl	7	82671867
answerals	CASE_NEULP998KDJ	als	sporadic	8	77194450
answerals	CASE_NEULT851ENP	als	sporadic	7	99055975
answerals	CASE_NEULY177TTN	als	sporadic	17	67915998
answerals	CASE_NEULY328CRJ	als	sporadic	13	84555017
answerals	CASE_NEULZ548ZXV	als	sporadic	20	83302460
answerals	CTRL_NEUMA002VLD	ctrl	ctrl	6	93511645
answerals	CASE_NEUMB242CLN	als	sporadic	14	104455218
answerals	CASE_NEUME498PCJ	als	sporadic	7	93041938
answerals	CTRL_NEUMF089KLV	ctrl	ctrl	7	77138477
answerals	CASE_NEUMH634MKT	als	sporadic	12	101656984
answerals	CTRL_NEUML507PFJ	ctrl	ctrl	8	68142265
answerals	CASE_NEUMN012EVP	als	sporadic	8	95638539
answerals	CTRL_NEUMN061ATZ	ctrl	ctrl	12	77105679
answerals	CTRL_NEUMT184NWC	ctrl	ctrl	12	92838742
answerals	CASE_NEUMW598YMT	als	sporadic	10	100036715
answerals	CASE_NEUMY871DGF	als	sporadic	5	109363694
answerals	CASE_NEUNA248WXL	als	sporadic	7	73992295
answerals	CTRL_NEUNC876ZB2	ctrl	ctrl	6	83265656
answerals	CASE_NEUNG326MFP	als	sporadic	7	100565728
answerals	CASE_NEUNJ155KYL	als	sporadic	7	95718335
answerals	CASE_NEUNJ938DUL	als	sporadic	11	75727499
answerals	CASE_NEUNL303HLF	als	sporadic	12	77783363
answerals	CASE_NEUNL415AFW	als	c9orf72	23	72450848
answerals	CTRL_NEUNN472ACB	ctrl	ctrl	15	78003255
answerals	CASE_NEUNR020KV6	als	sporadic	5	79298369
answerals	CTRL_NEUNW343RXP	ctrl	ctrl	9	67553681
answerals	CASE_NEUNZ171UYF	als	sporadic	5	113844202
answerals	CASE_NEUPG593UTV	als	sporadic	14	81542271
answerals	CTRL_NEUPH301NNX	ctrl	ctrl	12	81219399
answerals	CASE_NEUPK546ZLD	als	c9orf72	16	95071196
answerals	CTRL_NEUPL878MTL	ctrl	ctrl	10	88824097
answerals	CASE_NEUPM937TMY	als	sporadic	16	78296968
answerals	CASE_NEUPN525XEW	als	sporadic	29	77106625
answerals	CASE_NEUPR357AUF	als	sporadic	14	99795776
answerals	CASE_NEUPR600MBU	als	sporadic	12	78140048
answerals	CTRL_NEUPW536ZKZ	ctrl	ctrl	12	90333508
answerals	CASE_NEUPY050ANK	als	sporadic	9	85546543
answerals	CASE_NEURA639MK8	als	sporadic	17	65451477
answerals	CASE_NEURF720KHA	als	sporadic	13	84067184
answerals	CASE_NEURJ362MXH	als	setx	6	93645424
answerals	CTRL_NEURJ861MMD	ctrl	ctrl	10	100766856
answerals	CASE_NEURR881FKY	als	sporadic	12	86886855
answerals	CTRL_NEURV546WMW	ctrl	ctrl	13	111102498
answerals	CASE_NEURX315DYH	als	sporadic	10	73562711
answerals	CASE_NEURX909UL6	als	sporadic	19	79097478
answerals	CASE_NEUTA057AF6	als	sod1	12	108681404
answerals	CASE_NEUTA689LN5	als	sporadic	19	78200437
answerals	CASE_NEUTB230DA3	als	sporadic	13	89915287
answerals	CASE_NEUTC791PVE	als	sporadic	14	97793523
answerals	CASE_NEUTD314VFT	als	sporadic	9	85036873
answerals	CASE_NEUTD713DE3	als	sporadic	19	86563683
answerals	CASE_NEUTD866EAH	als	sporadic	6	83462831
answerals	CASE_NEUTJ613AH9	als	sporadic	6	60785280
answerals	CASE_NEUTL257PNR	als	sporadic	11	92252300
answerals	CASE_NEUTL699WJ0	als	sporadic	8	84089003
answerals	CASE_NEUTM934BPY	als	sporadic	5	95659466
answerals	CASE_NEUTN952DDG	als	c9orf72	25	81390541
answerals	CASE_NEUTU360YJY	als	sporadic	21	86008260
answerals	CASE_NEUUC458RFT	als	c9orf72	7	85257121
answerals	CASE_NEUUD158CJF	als	sporadic	4	73223524
answerals	CASE_NEUUE852BB1	als	sporadic	17	69679811
answerals	CASE_NEUUF613PHL	als	sporadic	9	88686748
answerals	CASE_NEUUH658EYH	als	sporadic	13	79107840
answerals	CASE_NEUUJ507EAB	als	sporadic	5	80476943
answerals	CASE_NEUUL256UC9	als	c9orf72	10	91609649
answerals	CASE_NEUUL292XRC	als	sporadic	10	72128909
answerals	CASE_NEUUL311NRQ	als	sporadic	9	100591313
answerals	CASE_NEUUP280KXL	als	sporadic	9	88847741
answerals	CASE_NEUUU506WZF	als	sporadic	3	91100217
answerals	CTRL_NEUUV825HYF	ctrl	ctrl	9	70624857
answerals	CASE_NEUUZ216XY8	als	sporadic	12	84742547
answerals	CASE_NEUVD687XD8	als	sporadic	13	85203015
answerals	CASE_NEUVF888UHM	als	sporadic	22	70783470
answerals	CASE_NEUVJ560JGZ	als	sporadic	8	115247071
answerals	CASE_NEUVM674HUA	als	sporadic	36	82820866
answerals	CASE_NEUVN746WKV	als	sporadic	27	84953195
answerals	CASE_NEUVP060WFG	als	sporadic	9	96612825
answerals	CASE_NEUVR636VTP	als	sporadic	15	95352910
answerals	CASE_NEUVR814YCY	als	sporadic	10	104088826
answerals	CASE_NEUVU735HU6	als	sporadic	11	84591201
answerals	CASE_NEUVV225XKB	als	sporadic	13	88170541
answerals	CASE_NEUVW680LPK	als	c9orf72	14	87448426
answerals	CASE_NEUVW999FP9	als	sporadic	11	88473708
answerals	CASE_NEUVX902YNL	als	c9orf72	5	81243471
answerals	CTRL_NEUVZ050YX7	ctrl	ctrl	7	77251633
answerals	CASE_NEUWD538KT3	als	c9orf72	18	74315992
answerals	CASE_NEUWD946RDA	als	sporadic	8	102258678
answerals	CASE_NEUWH380YHV	als	sporadic	6	90420294
answerals	CASE_NEUWH955FJF	als	sporadic	6	91413950
answerals	CASE_NEUWJ389FC7	als	sporadic	8	88864871
answerals	CASE_NEUWM344ZLM	als	sod1	9	80462242
answerals	CTRL_NEUWN092BVG	ctrl	ctrl	7	74745103
answerals	CASE_NEUWP426NBR	als	sporadic	12	85098806
answerals	CTRL_NEUWT164JRQ	ctrl	ctrl	9	103798428
answerals	CASE_NEUWX167DAH	als	sporadic	5	87911909
answerals	CASE_NEUWY079WUH	als	sporadic	10	75802121
answerals	CASE_NEUWZ614ARQ	als	sporadic	10	72558498
answerals	CTRL_NEUXC258VTR	ctrl	ctrl	15	103588765
answerals	CASE_NEUXD985ZU1	als	sporadic	8	92604037
answerals	CASE_NEUXE491RMM	als	sporadic	9	82047541
answerals	CASE_NEUXG265ME9	als	sporadic	10	93938146
answerals	CASE_NEUXH833LL4	als	sporadic	16	98829108
answerals	CASE_NEUXP289KRC	als	sod1	11	106091617
answerals	CASE_NEUXP495XE2	als	sporadic	13	100629256
answerals	CASE_NEUXP595FFP	als	sporadic	10	67304414
answerals	CTRL_NEUXP955XW7	ctrl	ctrl	8	82124112
answerals	CASE_NEUXV122FN3	als	sporadic	11	93817112
answerals	CTRL_NEUXW311EFC	ctrl	ctrl	8	56004456
answerals	CASE_NEUXX361RUG	als	sporadic	17	87796977
answerals	CASE_NEUYC055XPZ	als	sporadic	8	100062178
answerals	CASE_NEUYC303AJY	als	sporadic	3	86066890
answerals	CASE_NEUYD306HAB	als	sporadic	12	70161401
answerals	CASE_NEUYG208KVV	als	sporadic	8	80960435
answerals	CASE_NEUYG298CA9	als	sporadic	9	87861391
answerals	CASE_NEUYH924UCE	als	sporadic	12	89527986
answerals	CASE_NEUYJ705VN9	als	sporadic	12	83023955
answerals	CASE_NEUYK029WUU	als	sporadic	11	72565571
answerals	CASE_NEUYL008GM4	als	sporadic	6	96506969
answerals	CASE_NEUYL149PRF	als	sporadic	24	85694260
answerals	CTRL_NEUYM205MRL	ctrl	ctrl	17	89833971
answerals	CASE_NEUYP226CPW	als	sporadic	5	95899428
answerals	CASE_NEUYP235ZLD	als	tardbp	22	85518723
answerals	CASE_NEUYT193FFG	als	sporadic	6	95425283
answerals	CASE_NEUYY225MNZ	als	c9orf72	6	72067964
answerals	CASE_NEUYY614DN8	als	sporadic	9	94478114
answerals	CASE_NEUYY878JGP	als	sporadic	21	97959785
answerals	CASE_NEUZD231YZ6	als	sporadic	10	62725001
answerals	CASE_NEUZE432DZM	als	sporadic	3	89658284
answerals	CASE_NEUZF321EW4	als	sod1	13	88877063
answerals	CASE_NEUZF473UEP	als	sporadic	9	80160194
answerals	CASE_NEUZJ053JGZ	als	sporadic	11	90808999
answerals	CASE_NEUZK054DP5	als	sporadic	10	84338071
answerals	CASE_NEUZN836GME	als	sporadic	18	107877753
answerals	CASE_NEUZN936HJ9	als	sporadic	8	61443203
answerals	CASE_NEUZP278MR4	als	sporadic	13	94669647
answerals	CASE_NEUZT557DHF	als	sporadic	11	86771528
answerals	CASE_NEUZT902WVB	als	sporadic	8	108245336
answerals	CASE_NEUZV656DD1	als	sporadic	7	106453390
answerals	CASE_NEUZW701NNF	als	c9orf72	10	65392843
answerals	CASE_NEUZX521TKK	als	sporadic	15	76703248
answerals	CASE_NEUZX847VWV	als	sporadic	5	89949563
answerals	CASE_NEUZY128BJ2	als	sporadic	6	103216328
answerals	CASE_NEUZY975XKL	als	sporadic	12	75377931
neurolincs.diMN	00072034C01	ctrl	ctrl	4	88913175
neurolincs.diMN	00072034A01	ctrl	ctrl	7	87856433
neurolincs.diMN	00072028B01	ctrl	ctrl	5	84686602
neurolincs.diMN	00072028A01	ctrl	ctrl	6	86183708
neurolincs.diMN	00072010B02	ctrl	ctrl	10	71171884
neurolincs.diMN	00072010A02	ctrl	ctrl	7	72849067
neurolincs.diMN	00072002B02	ctrl	ctrl	8	81893396
neurolincs.diMN	00072002A02	ctrl	ctrl	8	78862140
neurolincs.diMN	00072007B01	als	sporadic	14	95655968
neurolincs.diMN	00072007A01	als	sporadic	16	86353118
neurolincs.diMN	00072006B01	als	sporadic	8	88663514
neurolincs.diMN	00072006A01	als	sporadic	8	87562150
neurolincs.diMN	00072019B02	als	sporadic	5	102600997
neurolincs.diMN	00072019A02	als	sporadic	4	81561535
neurolincs.diMN	00072001B02	als	sporadic	11	68658150
neurolincs.diMN	00072001A02	als	sporadic	5	92152108
neurolincs.diMN	00072029B01	als	sod1	9	88644882
neurolincs.diMN	00072029A01	als	sod1	9	69660338
neurolincs.diMN	00072018B02	als	sod1	6	92304897
neurolincs.diMN	00072018A02	als	sod1	5	78852703
neurolincs.diMN	00072008B02	als	sod1	4	93585462
neurolincs.diMN	00072008A02	als	sod1	6	80849799
neurolincs.diMN	00072015B01	als	c9orf72	5	81357232
neurolincs.diMN	00072015A01	als	c9orf72	8	80643118
neurolincs.diMN	00072012B01	als	c9orf72	9	77973353
neurolincs.diMN	00072012A01	als	c9orf72	9	96935001
neurolincs.diMN	00072014B02	als	c9orf72	7	92196364
neurolincs.diMN	00072014A02	als	c9orf72	3	83893881
neurolincs.diMN	00072013B02	als	c9orf72	8	35176115
neurolincs.diMN	00072013A02	als	c9orf72	6	77127001
neurolincs.iMN	A-042-52iALS-004	als	c9orf72	4	70623351
neurolincs.iMN	A-042-52iALS-003	als	c9orf72	3	54474248
neurolincs.iMN	A-042-30iALS-004	als	c9orf72	3	82501857
neurolincs.iMN	A-042-30iALS-003	als	c9orf72	3	62344831
neurolincs.iMN	A-042-29iALS-004	als	c9orf72	3	71722128
neurolincs.iMN	A-042-29iALS-003	als	c9orf72	4	67369943
neurolincs.iMN	A-042-28iALS-005	als	c9orf72	9	67180410
neurolincs.iMN	A-042-28iALS-003	als	c9orf72	8	50323795
neurolincs.iMN	A-042-83iCTR-004	ctrl	ctrl	6	59263773
neurolincs.iMN	A-042-83iCTR-003	ctrl	ctrl	4	59709899
neurolincs.iMN	A-042-25iCTR-004	ctrl	ctrl	8	79504132
neurolincs.iMN	A-042-25iCTR-003	ctrl	ctrl	7	61589963
neurolincs.iMN	A-042-00iCTR-004	ctrl	ctrl	4	64065133
neurolincs.iMN	A-042-00iCTR-003	ctrl	ctrl	3	58621934
catanese	c9orf72_6	als	c9orf72	12	44556823
catanese	c9orf72_5	als	c9orf72	18	44718862
catanese	c9orf72_4	als	c9orf72	16	48198617
catanese	c9orf72_3	als	c9orf72	11	49885755
catanese	c9orf72_2	als	c9orf72	9	50374657
catanese	c9orf72_1	als	c9orf72	12	52025076
catanese	ctrl_3	ctrl	ctrl	4	41908415
catanese	ctrl_2	ctrl	ctrl	8	41297925
catanese	ctrl_1	ctrl	ctrl	10	43538558
catanese	fus_6	als	fus	3	43123381
catanese	fus_5	als	fus	5	43486913
catanese	fus_4	als	fus	7	40817402
catanese	fus_3	als	fus	10	47197324
catanese	fus_2	als	fus	7	43048688
catanese	fus_1	als	fus	11	45844283
catanese	iso_3	ctrl	iso	9	48361792
catanese	iso_2	ctrl	iso	9	47178499
catanese	iso_1	ctrl	iso	6	44076352
dafinca.c9orf72	c9orf72_4	als	c9orf72	3	42732664
dafinca.c9orf72	c9orf72_3	als	c9orf72	3	41062026
dafinca.c9orf72	c9orf72_2	als	c9orf72	2	48882465
dafinca.c9orf72	c9orf72_1	als	c9orf72	3	46262015
dafinca.c9orf72	ctrl_1	ctrl	ctrl	5	45099717
dafinca.c9orf72	iso_7	ctrl	iso	1	36358941
dafinca.c9orf72	iso_6	ctrl	iso	2	36968802
dafinca.c9orf72	iso_5	ctrl	iso	1	39174051
dafinca.c9orf72	iso_4	ctrl	iso	4	35084625
dafinca.c9orf72	iso_3	ctrl	iso	4	45037650
dafinca.c9orf72	iso_2	ctrl	iso	4	41434677
dafinca.c9orf72	iso_1	ctrl	iso	2	43145131
dafinca.tardbp	ctrl_4	ctrl	ctrl	5	13922252
dafinca.tardbp	ctrl_3	ctrl	ctrl	11	18207019
dafinca.tardbp	ctrl_2	ctrl	ctrl	5	17409430
dafinca.tardbp	ctrl_1	ctrl	ctrl	7	15358338
dafinca.tardbp	tardbp_6	als	tardbp	3	18450547
dafinca.tardbp	tardbp_5	als	tardbp	6	16996133
dafinca.tardbp	tardbp_4	als	tardbp	5	17702995
dafinca.tardbp	tardbp_3	als	tardbp	5	18246091
dafinca.tardbp	tardbp_2	als	tardbp	8	16856043
dafinca.tardbp	tardbp_1	als	tardbp	5	17886729
wang	iso_2	ctrl	iso	9	25470901
wang	iso_1	ctrl	iso	16	25318736
wang	sod1_2	als	sod1	18	25959947
wang	sod1_1	als	sod1	14	26086164
kiskinis	iso_3	ctrl	iso	12	14683349
kiskinis	iso_2	ctrl	iso	9	9190834
kiskinis	iso_1	ctrl	iso	7	10724700
kiskinis	sod1_2	als	sod1	38	13383139
kiskinis	sod1_1	als	sod1	28	7037516
sommer	c9orf72_1	als	c9orf72	3	40465430
sommer	c9orf72_2	als	c9orf72	4	38631160
sommer	c9orf72_3	als	c9orf72	4	41347502
sommer	c9orf72_4	als	c9orf72	2	50500461
sommer	c9orf72_5	als	c9orf72	3	40439998
sommer	c9orf72_6	als	c9orf72	3	46224895
sommer	ctrl_1	ctrl	ctrl	2	41738904
sommer	ctrl_2	ctrl	ctrl	5	46986721
sommer	ctrl_3	ctrl	ctrl	7	43178218
sommer	ctrl_4	ctrl	ctrl	3	39573053
sommer	ctrl_5	ctrl	ctrl	4	40502175
sommer	ctrl_6	ctrl	ctrl	4	36822732
desantis	ctrl_3	ctrl	ctrl	5	29102751
desantis	ctrl_2	ctrl	ctrl	2	18402574
desantis	ctrl_1	ctrl	ctrl	8	27289409
desantis	fus_3	als	fus	9	26858355
desantis	fus_2	als	fus	2	16997472
desantis	fus_1	als	fus	8	24909519
smith	tardbp_3	als	tardbp	1	7448645
smith	tardbp_2	als	tardbp	1	7455838
smith	ctrl_2	ctrl	ctrl	1	7445173
smith	tardbp_1	als	tardbp	0	7471156
smith	ctrl_1	ctrl	ctrl	0	7248337

Table S10 RNA fusion burden

ipsc_mn_als_datasets.starfusion.als.count = ipsc_mn_als_datasets.starfusion %>% filter(condition == "als") %>% count(breakpoints, name = "als_yes")
ipsc_mn_als_datasets.starfusion.ctrl.count = ipsc_mn_als_datasets.starfusion %>% filter(condition == "ctrl") %>% count(breakpoints, name = "ctrl_yes")
ipsc_mn_als_datasets.starfusion.als_ctrl.prop = ipsc_mn_als_datasets.starfusion.als.count %>% full_join(ipsc_mn_als_datasets.starfusion.ctrl.count)  %>% replace(is.na(.),0) %>% 
  mutate(als_total = 360, ctrl_total = 90) 
ipsc_mn_als_datasets.starfusion.als_ctrl.burden = ipsc_mn_als_datasets.starfusion.als_ctrl.prop %>% rowwise() %>% 
  mutate(p_value = ClusterBurden::burden_test(n1 = als_yes, n2 = ctrl_yes, ss1 = als_total, ss2 = ctrl_total), 
         odds_ratio = ClusterBurden::burden_test(n1 = als_yes, n2 = ctrl_yes, ss1 = als_total, ss2 = ctrl_total, pval = FALSE)$estimate[1],
         lower_ci = ClusterBurden::burden_test(n1 = als_yes, n2 = ctrl_yes, ss1 = als_total, ss2 = ctrl_total, pval = FALSE)$conf.int[1], 
         upper_ci = ClusterBurden::burden_test(n1 = als_yes, n2 = ctrl_yes, ss1 = als_total, ss2 = ctrl_total, pval = FALSE)$conf.int[2])
als_ctrl.voila = read_voila_het(here(proj_path,"splicing/majiq_polyA/voila-tsv/als_vs_ctrl.tsv"), grp1 = "als", grp2 = "ctrl")
als_ctrl.voila.splicing_genes = als_ctrl.voila %>% filter(changing_dpsi0.1 == TRUE) %>% distinct(gene_name) %>% pull(gene_name)
ipsc_mn_als_datasets.starfusion.als_ctrl.burden_table = ipsc_mn_als_datasets.starfusion.als_ctrl.burden %>% left_join(distinct(select(ipsc_mn_als_datasets.starfusion, breakpoints, fusion_name, fusion_type))) %>% arrange(p_value) %>% 
  mutate(gene1 = str_split_fixed(fusion_name, "--",2)[,1], gene2 = str_split_fixed(fusion_name, "--",2)[,2], alterated_splicing = case_when(gene1 %in% als_ctrl.voila.splicing_genes | gene2 %in% als_ctrl.voila.splicing_genes ~ "yes", TRUE ~ "")) %>%
  select(fusion_name, breakpoints, fusion_type, als_number = als_yes, ctrl_number = ctrl_yes, odds_ratio, lower_ci, upper_ci, p_value, alterated_splicing)
kbl(ipsc_mn_als_datasets.starfusion.als_ctrl.burden_table) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), fixed_thead = T) %>% scroll_box(width = "100%", height = "500px")

fusion_name	breakpoints	fusion_type	als_number	ctrl_number	odds_ratio	lower_ci	upper_ci	p_value	alterated_splicing
LINC01572–PMFBP1	16:72284572:-_16:72164916:-	Neighbours	78	7	3.2728937	1.6047187	Inf	0.0011203
Z68871.1–LINC00630	X:102905737:+_X:102839801:+	Overlapping Neighbours	181	29	2.1234663	1.3759543	Inf	0.0014416
AC010332.1–ZNF880	19:52392788:+_19:52373111:+	Local Rearrangement	200	35	1.9613446	1.2874724	Inf	0.0032937
KRTAP5-AS1–AP000867.5	11:1598289:+_11:71563684:-	Distant Proximity	121	18	2.0221779	1.2305056	Inf	0.0075401
C15orf40–HOMER2	15:83010237:-_15:82892841:-	Neighbours	17	0	Inf	1.3333228	Inf	0.0208346
CAVIN4–TMEFF1	9:100585927:+_9:100498765:+	Local Rearrangement	165	30	1.6903608	1.0980652	Inf	0.0208959
CAVIN4–MSANTD3-TMEFF1	9:100585927:+_9:100498765:+	Local Rearrangement	165	30	1.6903608	1.0980652	Inf	0.0208959
LARS2–LIMD1	3:45524108:+_3:45636150:+	Neighbours	94	15	1.7648808	1.0352138	Inf	0.0385663
SPRY4-AS1–LINC01844	5:142581914:+_5:142745208:+	Neighbours	21	1	5.5008163	1.0360787	Inf	0.0441840
NREP–HMGN1P13	5:111735430:-_5:111572325:-	Neighbours	53	8	1.7675789	0.8839976	Inf	0.0979401
CRYBB2P1–GRK3	22:25520733:+_22:25604377:+	Neighbours	10	0	Inf	0.7222905	Inf	0.1046809
CRYBB2P1–GRK3	22:25459515:+_22:25604377:+	Neighbours	32	4	2.0948470	0.8181814	Inf	0.1166954
PTCHD4–AL353138.1	6:48008634:-_6:48069926:-	Local Rearrangement	41	6	1.7974036	0.8173929	Inf	0.1297229
CRIPT–SOCS5	2:46619681:+_2:46758519:+	Neighbours	9	0	Inf	0.6372242	Inf	0.1315222
SUB1–NPR3	5:32591685:+_5:32724698:+	Neighbours	14	1	3.5943007	0.6480436	Inf	0.1624562
NKAPP1–RHOXF1	X:120244764:-_X:120112914:-	Neighbours	8	0	Inf	0.5528235	Inf	0.1651500
ZNF350-AS1–ZNF613	19:51949235:+_19:51948593:+	Local Rearrangement	108	22	1.3238770	0.8260146	Inf	0.1819186
PHOSPHO2–SSB	2:169697531:+_2:169800952:+	Neighbours	28	4	1.8111304	0.6999659	Inf	0.1945447
KLHL23–SSB	2:169697531:+_2:169800952:+	Neighbours	28	4	1.8111304	0.6999659	Inf	0.1945447
AC119396.2–INSR	19:7390576:-_19:7267896:-	Neighbours	31	5	1.6002867	0.6737518	Inf	0.2356393
LINC01572–PMFBP1	16:72284577:-_16:72164916:-	Neighbours	16	2	2.0438043	0.5539893	Inf	0.2658738
AL035409.2–ST6GALNAC5	1:77068843:+_1:77044204:+	Local Rearrangement	34	6	1.4589710	0.6548855	Inf	0.2747451
USP22–RN7SL5P	17:21000941:-_9:9442093:+	Inter Chromosomal	29	5	1.4882051	0.6230567	Inf	0.2903729
CCDC122–ENOX1	13:43874842:-_13:43667544:-	Neighbours	84	18	1.2169099	0.7323742	Inf	0.3003889
CXXC5–PSD2	5:139648845:+_5:139809391:+	Distant Proximity	15	2	1.9107102	0.5134713	Inf	0.3047402
DR1–FNBP1L	1:93354071:+_1:93499468:+	Neighbours	10	1	2.5389621	0.4341852	Inf	0.3188009
AC068446.2–NBEAP1	15:21298706:-_15:20671268:-	Distant Proximity	5	0	Inf	0.3042365	Inf	0.3258509
AC068446.2–AC060814.4	15:21324988:-_15:21328980:-	Local Rearrangement	5	0	Inf	0.3042365	Inf	0.3258509
CAVIN4–MSANTD3-TMEFF1	9:100578551:+_9:100498765:+	Local Rearrangement	5	0	Inf	0.3042365	Inf	0.3258509
CAVIN4–TMEFF1	9:100578551:+_9:100498765:+	Local Rearrangement	5	0	Inf	0.3042365	Inf	0.3258509
SEPTIN7P2–PSPH	7:45768660:-_7:56021231:-	Distant Proximity	19	3	1.6142971	0.5366688	Inf	0.3262606
PSPC1–ZMYM5	13:19730239:-_13:19835689:-	Local Rearrangement	52	11	1.2120054	0.6523621	Inf	0.3625712
YAF2–RYBP	12:42238155:-_3:72446621:-	Inter Chromosomal	9	1	2.2788627	0.3817097	Inf	0.3730935
C1QTNF3-AMACR–AC138409.2	5:34124418:-_5:34182867:-	Local Rearrangement	9	1	2.2788627	0.3817097	Inf	0.3730935
LINC01145–SRGAP2B	1:145215639:-_1:145093443:-	Neighbours	13	2	1.6468051	0.4332582	Inf	0.3946244
CRYBB2P1–GRK3	22:25520733:+_22:25644592:+	Neighbours	17	3	1.4362559	0.4709196	Inf	0.4074672
FNBP1L–MTF2	1:93448305:+_1:93110230:+	Distant Proximity	4	0	Inf	0.2236690	Inf	0.4082290
MFNG–ELFN2	22:37476896:-_22:37375996:-	Neighbours	4	0	Inf	0.2236690	Inf	0.4082290
RARB–AC092422.1	3:25174575:+_3:25060125:+	Local Rearrangement	4	0	Inf	0.2236690	Inf	0.4082290
PCMTD1–PXDNL	8:51898930:-_8:51654760:-	Neighbours	4	0	Inf	0.2236690	Inf	0.4082290
LINC01933–AC008571.2	5:152267355:+_5:152537704:+	Distant Proximity	8	1	2.0201305	0.3296629	Inf	0.4341726
NREP–HMGN1P13	5:111677940:-_5:111572325:-	Neighbours	28	6	1.1803022	0.5211467	Inf	0.4620774
AC012414.4–AC012414.2	15:20755930:-_15:20761158:-	Local Rearrangement	11	2	1.3858939	0.3542338	Inf	0.4999902
MFSD14B–AL691447.2	9:94438528:+_9:94338836:+	Local Rearrangement	7	1	1.7629706	0.2781994	Inf	0.5019998
AC092745.5–AC092745.2	12:8251507:+_12:8368982:+	Overlapping Neighbours	3	0	Inf	0.1453421	Inf	0.5111438
PSPC1–ZMYM5	13:19741565:-_13:19835689:-	Local Rearrangement	3	0	Inf	0.1453421	Inf	0.5111438
LINC00674–AMZ2	17:68114525:+_17:68250188:+	Neighbours	3	0	Inf	0.1453421	Inf	0.5111438
AC022140.1–AC099499.1	5:25445820:-_5:25298609:-	Distant Proximity	3	0	Inf	0.1453421	Inf	0.5111438
MRPL14–AL109615.3	6:44114077:-_6:44001116:-	Neighbours	3	0	Inf	0.1453421	Inf	0.5111438
KIAA1958–SNX30	9:112575251:+_9:112804776:+	Neighbours	3	0	Inf	0.1453421	Inf	0.5111438
TMEM164–RTL9	X:110171520:+_X:110445153:+	Distant Proximity	3	0	Inf	0.1453421	Inf	0.5111438
AL353138.1–PTCHD4	6:48111049:-_6:48009114:-	Neighbours	293	73	1.0183603	0.5851179	Inf	0.5285463
LINC01315–TCF20	22:42368715:-_22:42215341:-	Neighbours	34	8	1.0688608	0.5190433	Inf	0.5294393
PRDM2–KAZN	1:13843107:+_1:14180435:+	Neighbours	18	4	1.1312776	0.4170870	Inf	0.5418132
BBOX1-AS1–SLC5A12	11:27151050:-_11:26712706:-	Distant Proximity	14	3	1.1730102	0.3739383	Inf	0.5479357
AC022523.1–LINC00923	15:98046667:-_15:97758661:-	Distant Proximity	14	3	1.1730102	0.3739383	Inf	0.5479357
AC007318.1–RAB1A	2:65227485:-_2:65104806:-	Neighbours	6	1	1.5072445	0.2273381	Inf	0.5761020
SHROOM2–WWC3	X:9944913:+_X:10063445:+	Neighbours	17	4	1.0654241	0.3897934	Inf	0.5860321
USP22–RN7SL2	17:21000941:-_14:49862816:-	Inter Chromosomal	36	9	1.0000000	0.5019715	Inf	0.5896286
USP22–AL627171.2	17:21000941:-_14:49862816:-	Inter Chromosomal	36	9	1.0000000	0.5019715	Inf	0.5896286
AC096642.2–LYPLAL1	1:219295151:+_1:219193082:+	Local Rearrangement	2	0	Inf	0.0718293	Inf	0.6396437
MIR3659HG–AL390839.2	1:38047614:+_1:38209271:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
SLIRP–ADCK1	14:77715879:+_14:77818968:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
ADAMTSL3–SH3GL3	15:83704508:+_15:83559253:+	Local Rearrangement	2	0	Inf	0.0718293	Inf	0.6396437
AC110023.1–AC091078.1	15:93592733:+_15:93553605:+	Local Rearrangement	2	0	Inf	0.0718293	Inf	0.6396437
LINC01572–PMFBP1	16:72284577:-_16:72171268:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
LINC01572–PMFBP1	16:72284577:-_16:72174070:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
CDH13–HSBP1	16:83486655:+_16:83808680:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
USP22–RN7SL4P	17:21000941:-_3:15738548:+	Inter Chromosomal	2	0	Inf	0.0718293	Inf	0.6396437
ARL17B–KANSL1	17:46352820:-_17:46094701:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
SNHG16–METTL23	17:76614005:+_17:76732978:+	Distant Proximity	2	0	Inf	0.0718293	Inf	0.6396437
SNHG16–AC005837.2	17:76614005:+_17:76732978:+	Distant Proximity	2	0	Inf	0.0718293	Inf	0.6396437
RNF138–MEP1B	18:32092886:+_18:32204182:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
MYDGF–SEMA6B	19:4659931:-_19:4558489:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
AC007318.1–RAB1A	2:65227428:-_2:65104806:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
RNF144A–AC013460.1	2:7075684:+_2:7421184:+	Distant Proximity	2	0	Inf	0.0718293	Inf	0.6396437
MBOAT2–KIDINS220	2:9003540:-_2:8827129:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
CHMP4B–DEFB115	20:33811658:+_20:31249509:+	Distant Proximity	2	0	Inf	0.0718293	Inf	0.6396437
C20orf194–PIAS1	20:3407475:-_15:68086306:+	Inter Chromosomal	2	0	Inf	0.0718293	Inf	0.6396437
PPM1F–MAPK1	22:21931148:-_22:21807846:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
CRYBB2P1–GRK3	22:25520733:+_22:25667739:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
SYN3–RTCB	22:32596674:-_22:32408833:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
AC068756.1–AC108690.1	3:80761438:-_3:80633971:-	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
AC026410.3–RAPGEF1	5:80351234:-_9:131650949:-	Inter Chromosomal	2	0	Inf	0.0718293	Inf	0.6396437	yes
MAILR–ATP6V1C1	8:102880058:+_8:103040798:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
LINC00032–EQTN	9:27277551:-_9:27286362:-	Overlapping Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
ERMP1–KIAA2026	9:5787061:-_9:5914062:-	Local Rearrangement	2	0	Inf	0.0718293	Inf	0.6396437
BHLHB9–LINC00630	X:102721027:+_X:102826969:+	Neighbours	2	0	Inf	0.0718293	Inf	0.6396437
PFKFB3–LINC02649	10:6226365:+_10:6326546:+	Neighbours	23	6	0.9555832	0.4134739	Inf	0.6453096
BTF3L4–ZFYVE9	1:52064938:+_1:52216369:+	Neighbours	5	1	1.2529300	0.1772958	Inf	0.6553631
EFL1P1–AC243562.2	15:84126600:+_15:84389732:+	Distant Proximity	5	1	1.2529300	0.1772958	Inf	0.6553631
LINC01572–PMFBP1	16:72284572:-_16:72171268:-	Neighbours	5	1	1.2529300	0.1772958	Inf	0.6553631
TIAM2–SCAF8	6:154944206:+_6:154773989:+	Overlapping Neighbours	5	1	1.2529300	0.1772958	Inf	0.6553631
AC009315.1–FSIP2	2:185434191:+_2:185739346:+	Distant Proximity	19	5	0.9473240	0.3790265	Inf	0.6580759
AC010332.1–ZNF880	19:52392776:+_19:52315951:+	Local Rearrangement	8	2	1.0000000	0.2381207	Inf	0.6784890
BHLHB9–LINC00630	X:102721091:+_X:102826969:+	Neighbours	8	2	1.0000000	0.2381207	Inf	0.6784890
LINC01145–SRGAP2	1:145215639:-_1:206205429:+	Distant Proximity	11	3	0.9142135	0.2790496	Inf	0.6997665
AC068446.2–AC060814.4	15:21324988:-_15:21330231:-	Local Rearrangement	4	1	1.0000000	0.1284236	Inf	0.7377411
LINC01572–PMFBP1	16:72249163:-_16:72164916:-	Neighbours	4	1	1.0000000	0.1284236	Inf	0.7377411
RPL37A–AC073321.1	2:216500031:+_2:216610744:+	Neighbours	4	1	1.0000000	0.1284236	Inf	0.7377411
MED27–RAPGEF1	9:131884058:-_9:131650949:-	Distant Proximity	4	1	1.0000000	0.1284236	Inf	0.7377411	yes
AL355987.4–GPI	9:136802786:+_19:34393248:+	Inter Chromosomal	34	10	0.8346905	0.4270443	Inf	0.7554429
CCDC183–GPI	9:136802786:+_19:34393248:+	Inter Chromosomal	34	10	0.8346905	0.4270443	Inf	0.7554429
AL355987.2–GPI	9:136802786:+_19:34393248:+	Inter Chromosomal	34	10	0.8346905	0.4270443	Inf	0.7554429
AEBP2–AC024901.1	12:19720691:+_12:19923198:+	Neighbours	9	3	0.7441212	0.2171197	Inf	0.7969835
PTS–LINC02762	11:112237506:+_11:112360069:+	Neighbours	6	2	0.7462908	0.1628979	Inf	0.7984087
AC009315.1–FSIP2	2:185502739:+_2:185739346:+	Distant Proximity	6	2	0.7462908	0.1628979	Inf	0.7984087
SLC16A1-AS1–LRIG2	1:112960575:+_1:113091318:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
MIIP–MFN2	1:12022917:+_1:12014257:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
LINC01138–FCGR1CP	1:148459602:-_1:143880437:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
RABGAP1L–MED14	1:174278779:+_X:40729345:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CEP350–TDRD5	1:179987286:+_1:179690696:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
CNIH3–WDR26	1:224434862:+_1:224431781:-	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AKT3–RPS10	1:243504348:-_6:34426060:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AKT3–RPS10	1:243504388:-_6:34426060:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
GMEB1–SMG1	1:28683740:+_16:18896956:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZFP69B–LIFR	1:40457071:+_5:38511964:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PLK3–RPS8	1:44802855:+_1:44778000:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
BTF3L4–ZFYVE9	1:52064938:+_1:52199251:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AL669831.3–DCX	1:631066:-_X:111299018:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
INPP5F–NOVA1	10:119823199:+_14:26448963:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
BX322639.1–CCNYL2	10:42360949:-_10:42433354:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
BX322639.1–CCNYL2	10:42367816:-_10:42433354:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
AGAP4–PARGP1	10:45841653:-_10:45964871:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
MRPS16–CFAP70	10:73251763:-_10:73256416:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
GLUD1P3–LMBR1	10:73730873:+_7:156826744:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
POLR3A–HMGCS1	10:78002197:-_5:43297166:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
NUTM2B-AS1–SFTPA2	10:79766271:-_10:79559506:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
NRG3–KRTAP5-AS1	10:81876163:+_11:1597248:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
NUTM2A-AS1–GLUD1	10:87326630:-_10:87076656:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
NKAPD1–CPSF3	11:112075643:+_2:9441818:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZBTB16–NXPE2	11:114187038:+_11:114679657:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
JAM3–PTPN11	11:134069159:+_12:112446276:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
DNAJC24–IMMP1L	11:31370859:+_11:31433570:-	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
PTDSS2–DLGAP4	11:448419:+_20:36525851:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PHF21A–SPCS2	11:46076754:-_11:74965034:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
PTDSS2–DAZAP1	11:479152:+_19:1417500:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
MALAT1–ADGRV1	11:65505019:+_5:90720935:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PACS1–UQCC2	11:66210451:+_6:33700513:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
RBM14–C11orf80	11:66617057:+_11:66759043:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000	yes
UBE3B–MYCN	12:109526416:+_2:15945493:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CFAP73–TSPAN4	12:113152887:+_11:847201:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
NCOR2–AC048337.1	12:124362126:-_12:121391970:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000	yes
NCOR2–RNF34	12:124362126:-_12:121416159:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000	yes
AEBP2–AC024901.1	12:19518113:+_12:19923198:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
BICD1–FGD4	12:32107544:+_12:32564137:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
TUBA1A–PTMS	12:49185036:-_12:6770157:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
TUBA1A–NEFM	12:49185049:-_8:24917437:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CPSF6–JAG2	12:69262562:+_14:105141329:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CPSF6–ADGRB3	12:69262562:+_6:69372406:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CPSF6–KBTBD11	12:69262562:+_8:2006545:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
TBC1D15–RAB21	12:71885021:+_12:71782031:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
PPP1R12A–CAPS2-AS1	12:79817394:-_12:75250493:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
ATP2B1–PCSK2	12:89627678:-_20:17436708:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PSPC1–ZMYM5	13:19772242:-_13:19835689:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
PSPC1–ZMYM5	13:19782386:-_13:19852190:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
PSPC1–ZMYM5	13:19782386:-_13:19862466:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
TPTE2P5–MRPS31	13:40908159:-_13:40767033:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
VWA8–SH3YL1	13:41865736:-_2:230044:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
FARP1–IPO5	13:98213413:+_13:98006130:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
SLC38A6–PRKCH	14:61030523:+_14:61391225:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC012414.4–IGHV1OR15-6	15:20755930:-_15:20639759:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AC012414.4–AC100757.2	15:20755930:-_15:22360628:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC124312.1–ATP2B1	15:24982437:+_12:89656107:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
TTBK2–SMG1P3	15:42827928:-_16:21487616:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AC012405.1–AC073941.1	15:46410353:+_15:46791282:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
MIR4713HG–GLDN	15:51037681:+_15:51377449:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
MIR4713HG–GLDN	15:51037681:+_15:51383785:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
TLN2–TLN1	15:62833629:+_9:35698679:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
UBE2Q2P2–AC243562.1	15:82355374:+_15:84394809:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
C15orf40–HOMER2	15:83008548:-_15:82892841:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
IGF1R–LUNAR1	15:98708107:+_15:99024600:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
IGF1R–SYNM	15:98708107:+_15:99113591:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
PDXDC1–NOMO1	16:15030056:+_16:14838407:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
PKD1P6–MAZ	16:15133264:-_16:29808230:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
UQCRC2–PJA2	16:21976243:+_5:109356026:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
DNASE1–PTBP1	16:3612006:+_19:799413:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
UTP4–SNTB2	16:69143389:+_16:69245602:+	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
ANKRD11–CAMK2B	16:89418284:-_7:44263064:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000	yes
DEF8–OSGEP	16:89949513:+_14:20449301:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000	yes
C16orf72–AC012178.1	16:9103259:+_16:9461042:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
SUZ12P1–AC005562.1	17:30709811:+_17:30631755:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000	yes
AC138207.8–NF1	17:31045549:+_17:31155983:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
ARL17A–KANSL1	17:46570759:-_17:46094701:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
NPEPPS–TBC1D3I	17:47592545:+_17:36262986:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
SPOP–MSI2	17:49677933:-_17:57401379:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
GDPD1–GIT2	17:59272836:+_12:109961685:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
TANC2–PSMA3	17:63319090:+_14:58247750:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
VPS53–GLOD4	17:710533:-_17:785977:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
ATP1B2–AL627171.2	17:7656775:+_14:49862816:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
FAAP100–TRIOBP	17:81544004:-_22:37733298:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
TBCD–ATP8B3	17:82911789:+_19:1800449:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
NTN1–AL121594.1	17:9221242:+_14:35266727:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
DOK6–AC006305.1	18:69599498:+_18:56181474:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC124254.2–AC123786.1	18:77422024:+_18:77613183:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
EIF3G–ENO4	19:10118668:-_10:116862799:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
TCF3–CCDC57	19:1646355:-_17:82101866:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZNF493–CRCP	19:21397267:+_7:66127704:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AC092279.1–RBM39	19:24049014:+_20:35725155:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
NFIC–CTBP1	19:3359685:+_4:1241519:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZBTB7A–WDR59	19:4053971:-_16:74956610:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZNF175–SRP68	19:51581882:+_17:76040474:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZNF846–ZNF561-AS1	19:9785938:-_19:9627716:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000	yes
KLHL23–SSB	2:169695247:+_2:169800952:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AC074286.1–AC092162.2	2:177094575:-_2:176812193:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC021851.1–NUP35	2:183182680:+_2:183151508:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
AC009315.1–FSIP2	2:185434187:+_2:185737013:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC009315.1–FSIP2	2:185502739:+_2:185737013:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC097500.1–LINC01473	2:185950644:-_2:186086370:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
WDR75–KLF7	2:189441578:+_2:207124404:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
PUM2–SMAD5	2:20327310:-_5:136160856:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
MAPRE3–ACYP2	2:26970802:+_2:54135453:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
LTBP1–BIRC6	2:33021206:+_2:32406499:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
CRIPT–SOCS5	2:46648138:+_2:46758519:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
SPTBN1–RTN4	2:54558887:+_2:54987698:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC007318.1–RAB1A	2:65227481:-_2:65104806:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
RNF144A–AC013460.1	2:6997061:+_2:7421184:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AL050403.2–PLXDC2	20:10673025:+_10:20211669:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PTPRA–ATF2	20:2965202:+_2:175151158:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
FASTKD5–CARS2	20:3159766:-_13:110701555:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
TTI1–VSTM2L	20:37996375:-_20:37931635:+	Local Inversion	1	0	Inf	0.0131578	Inf	0.8000000
SULF2–CKS1B	20:47736703:-_1:154978725:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
VAPB–APCDD1L-DT	20:58418363:+_20:58547812:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
CDH4–ERICH1	20:61254937:+_8:674047:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
VPS26C–TTC3	21:37267238:-_21:37108392:+	Local Inversion	1	0	Inf	0.0131578	Inf	0.8000000
GUSBP11–AC113385.3	22:23659865:-_5:100381398:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CRYBB2P1–GRK3	22:25459515:+_22:25661576:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
GALR3–ANKRD54	22:37823765:+_22:37838593:-	Local Inversion	1	0	Inf	0.0131578	Inf	0.8000000
RAB43–GORASP2	3:129121286:-_2:170948350:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
LINC02021–COL6A4P2	3:130112744:+_3:130212823:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
ANAPC13–AMOTL2	3:134482806:-_3:134371494:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC046134.2–CLSTN2	3:139583880:+_3:140175951:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
TFDP2–XRN1	3:142101735:-_3:142365366:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
METTL6–SH3BP5	3:15415772:-_3:15304231:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
EGFEM1P–KANSL1L	3:168824925:+_2:210154611:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
SLC7A14-AS1–AC061708.1	3:170708194:+_3:170994873:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
ERC2–CDC42BPA	3:56007181:-_1:227048010:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
UBA3–STRAP	3:69077798:-_12:15890597:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
ZNF717–RPL23AP49	3:75741276:-_3:75625153:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AC068756.1–AC108690.1	3:80761458:-_3:80633971:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
LINC02050–LINC02027	3:80770955:+_3:81095112:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC107029.2–COL8A1	3:99635662:+_3:99744897:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AC108865.1–AC110772.2	4:186891368:+_4:187019821:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
PCDH7–LINC02497	4:30920369:+_4:31176621:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
CFAP299–BMP3	4:80870135:+_4:81045738:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
EPB41L4A–NREP	5:112168739:-_5:111735507:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AP3S1–LVRN	5:115870128:+_5:115983287:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
LYRM7–CDC42SE2	5:131171038:+_5:131315976:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
UIMC1–UBE2B	5:177022464:-_5:134390225:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
C1QTNF3-AMACR–AC138409.2	5:34020587:-_5:34182867:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
TTC33–PRKAA1	5:40728345:-_5:40777586:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
RTN4IP1–LINC02532	6:106583328:-_6:106718242:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
CEP57L1–AFG1L	6:109095575:+_6:108519697:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
CEP57L1–TTC6	6:109146937:+_14:37807320:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
NEDD9–AC018630.4	6:11232504:-_12:11047188:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
RNF217–TPD52L1	6:125045444:+_6:125220078:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AL133346.1–AL021408.1	6:131951254:+_6:132098931:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
TBPL1–AHI1	6:133953425:+_6:135290525:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
CCDC170–TBC1D32	6:151615679:+_6:121239188:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
ATXN1–ZFYVE27	6:16753233:-_10:97744729:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AL662797.1–TUBA1A	6:30723725:-_12:49185063:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
RNF5–TSBP1-AS1	6:32180126:+_6:32365780:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AL021368.4–UBE4B	6:57959028:-_1:10161142:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
KHDRBS2-OT1–KHDRBS2	6:61652255:-_6:61707403:-	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
EEF1A1–RPS11	6:73519337:-_19:49497194:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AL391840.3–SH3BGRL2	6:79631633:+_6:79673614:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
EEF1E1-BLOC1S5–SMIM13	6:8097267:-_6:11134403:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
CNPY4–RPS6KC1	7:100119862:+_1:213242569:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
FAM185A–LRRC17	7:102808351:+_7:102933774:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
FBXL13–AC105052.5	7:102822040:-_7:102703527:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
RINT1–AC073073.1	7:105548710:+_7:105530509:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
MTPN–CHRM2	7:135977029:-_7:137014820:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
IQCE–SH3RF1	7:2573488:+_4:169156679:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
HOTAIRM1–WDR75	7:27096388:+_2:189458753:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
OGDH–ZNF816-ZNF321P	7:44681848:+_19:52929414:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AC079781.5–BBS2	7:97854580:-_16:56485738:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
BUD31–PDAP1	7:99410169:+_7:99400424:-	Local Inversion	1	0	Inf	0.0131578	Inf	0.8000000
DLC1–TPTEP2	8:13604537:-_22:38336706:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AC138696.1–MOB3A	8:143251340:+_19:2078679:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AC138696.1–ELFN1	8:143251340:+_7:1709091:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PLEC–PARP10	8:143975177:-_8:143986438:-	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000	yes
HGH1–CPSF6	8:144137958:+_12:69239612:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
RB1CC1–AMN1	8:52645702:-_12:31672377:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
CAVIN4–MSANTD3-TMEFF1	9:100578551:+_9:100576516:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
CAVIN4–MSANTD3-TMEFF1	9:100586185:+_9:100498765:+	Local Rearrangement	1	0	Inf	0.0131578	Inf	0.8000000
PTGR1–CHD9	9:111592926:-_16:53235185:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000	yes
KIAA1958–SNX30	9:112487118:+_9:112804776:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
INIP–C9orf147	9:112717987:-_9:112487090:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
ZBTB34–RALGPS1	9:126860739:+_9:126962225:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
PRRC2B–ATP5PO	9:131432794:+_21:33904021:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
PRRC2B–ANP32B	9:131439061:+_9:98015364:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
MED27–RAPGEF1	9:131853035:-_9:131650949:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000	yes
AL355987.4–MAN1B1	9:136808326:+_9:137088075:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
LINC00032–EQTN	9:27271927:-_9:27286362:-	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
LINC00032–EQTN	9:27274090:-_9:27286362:-	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
OSTF1–APP	9:75088726:+_21:26112146:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
MT-CO3–AL450405.1	MT:9411:+_6:117998990:+	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
Z68871.1–LINC00630	X:102905737:+_X:102816992:+	Overlapping Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
TMEM164–RTL9	X:110173453:+_X:110445153:+	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC073529.1–MID1	X:11038603:-_X:10567603:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
AC073529.1–MID1	X:11106901:-_X:10567603:-	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
DDX3X–STK39	X:41337465:+_2:167964726:-	Inter Chromosomal	1	0	Inf	0.0131578	Inf	0.8000000
AL050309.1–KLF8	X:55908800:+_X:56250231:+	Neighbours	1	0	Inf	0.0131578	Inf	0.8000000
NLGN4X–AC110995.1	X:5844434:-_X:5669606:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
PNPLA4–ANOS1	X:7912028:-_X:8623670:-	Distant Proximity	1	0	Inf	0.0131578	Inf	0.8000000
AC074286.1–AC073636.1	2:177094575:-_2:176846377:-	Distant Proximity	15	5	0.7396686	0.2857924	Inf	0.8087205
SPSB4–PXYLP1	3:141066798:+_3:141260123:+	Neighbours	204	55	0.8325058	0.5428240	Inf	0.8109189
AC079313.1–NCKAP1L	12:54353896:+_12:54499355:+	Neighbours	3	1	0.7484239	0.0815259	Inf	0.8198884
AC010332.1–ZNF880	19:52392788:+_19:52369787:+	Local Rearrangement	3	1	0.7484239	0.0815259	Inf	0.8198884
RNF144A–AC013460.1	2:7030215:+_2:7421184:+	Distant Proximity	3	1	0.7484239	0.0815259	Inf	0.8198884
B3GALT5–AF064860.1	21:39605225:+_21:39725838:+	Overlapping Neighbours	3	1	0.7484239	0.0815259	Inf	0.8198884
SLC38A9–MTREX	5:55697846:-_5:55340010:+	Distant Proximity	3	1	0.7484239	0.0815259	Inf	0.8198884
AC009315.1–FSIP2	2:185483091:+_2:185739346:+	Distant Proximity	154	43	0.8174895	0.5395972	Inf	0.8349925
AC012414.4–AC012414.2	15:20755930:-_15:20759907:-	Local Rearrangement	8	3	0.6597751	0.1866294	Inf	0.8413667
MFSD14C–ZNF510	9:96918643:-_9:96776245:-	Distant Proximity	8	3	0.6597751	0.1866294	Inf	0.8413667
CHST9–AQP4	18:27024116:-_18:26862596:-	Neighbours	5	2	0.6204697	0.1262877	Inf	0.8535156
AC120193.1–AC023202.1	8:24296154:-_8:24183283:-	Distant Proximity	5	2	0.6204697	0.1262877	Inf	0.8535156
BNIP3L–DPYSL2	8:26408376:+_8:26581969:+	Neighbours	5	2	0.6204697	0.1262877	Inf	0.8535156
TMED3–MINAR1	15:79314005:+_15:79456098:+	Neighbours	7	3	0.5758904	0.1565446	Inf	0.8815093
LINC00869–SEC22B3P	1:149607554:+_1:148779001:-	Distant Proximity	2	1	0.4981146	0.0386191	Inf	0.8966433
CSTF3–FBXO3	11:33141667:-_11:33742084:-	Distant Proximity	2	1	0.4981146	0.0386191	Inf	0.8966433
AP002784.1–PANX1	11:94189083:+_11:94178369:+	Local Rearrangement	2	1	0.4981146	0.0386191	Inf	0.8966433
PSPC1–ZMYM5	13:19759323:-_13:19835689:-	Local Rearrangement	2	1	0.4981146	0.0386191	Inf	0.8966433
USP22–RN7SL1	17:21000941:-_14:49586613:+	Inter Chromosomal	2	1	0.4981146	0.0386191	Inf	0.8966433
PANTR1–LINC01114	2:104851312:-_2:104749489:-	Neighbours	2	1	0.4981146	0.0386191	Inf	0.8966433
MMP24OS–EDEM2	20:35272005:-_20:35142478:-	Neighbours	2	1	0.4981146	0.0386191	Inf	0.8966433
AC025465.1–LINC01170	5:124512257:-_5:124386273:-	Neighbours	2	1	0.4981146	0.0386191	Inf	0.8966433
ZNRF2P1–FKBP9	7:32729009:+_7:32974617:+	Distant Proximity	2	1	0.4981146	0.0386191	Inf	0.8966433
OR51S1–TP53I11	11:4848669:-_11:44933050:-	Distant Proximity	6	3	0.4924414	0.1269719	Inf	0.9164357
KHDRBS2-OT1–KHDRBS2	6:61652255:-_6:61732764:-	Overlapping Neighbours	6	3	0.4924414	0.1269719	Inf	0.9164357
LINC01572–PMFBP1	16:72284572:-_16:72174070:-	Neighbours	5	3	0.4094664	0.0980454	Inf	0.9453606
PPP4R3B–CCDC88A	2:55558775:-_2:55418916:-	Distant Proximity	7	4	0.4273438	0.1283228	Inf	0.9517590
AL050309.1–KLF8	X:56014961:+_X:56250231:+	Neighbours	7	4	0.4273438	0.1283228	Inf	0.9517590
AL391840.3–SH3BGRL2	6:79538843:+_6:79673614:+	Neighbours	272	75	0.6187910	0.3493038	Inf	0.9596768
ST6GALNAC5–AL035409.2	1:76868742:+_1:77068084:+	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
AC093423.3–ZNF326	1:89843782:+_1:89998110:+	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
RAB38–AP000676.5	11:88149675:-_11:87954906:-	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
TUBA1B–PTMS	12:49127984:-_12:6770157:+	Distant Proximity	1	1	0.2489532	0.0064235	Inf	0.9603563
TUBA1A–PTMS	12:49127984:-_12:6770157:+	Distant Proximity	1	1	0.2489532	0.0064235	Inf	0.9603563
SMG1–NPIPB13	16:18858170:-_16:30245853:-	Distant Proximity	1	1	0.2489532	0.0064235	Inf	0.9603563
LINC01572–PMFBP1	16:72249163:-_16:72171268:-	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
LINC01572–PMFBP1	16:72267552:-_16:72164916:-	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
PDK1–RAPGEF4	2:172670884:+_2:172795025:+	Distant Proximity	1	1	0.2489532	0.0064235	Inf	0.9603563
ABLIM2–AFAP1	4:7983264:-_4:7872080:-	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
KHDRBS2-OT1–KHDRBS2	6:61652255:-_6:61697253:-	Overlapping Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
FAM155B–EDA	X:69506143:+_X:69957027:+	Neighbours	1	1	0.2489532	0.0064235	Inf	0.9603563
LINC01933–AC008571.2	5:152270360:+_5:152537704:+	Distant Proximity	3	3	0.2447366	0.0435728	Inf	0.9837215
TVP23C–AC005703.3	17:15540433:-_17:15416233:-	Neighbours	1	2	0.1233715	0.0042115	Inf	0.9922129	yes
TVP23C-CDRT4–AC005703.3	17:15540433:-_17:15416233:-	Neighbours	1	2	0.1233715	0.0042115	Inf	0.9922129	yes
AC074286.1–AC092162.2	2:177094575:-_2:176792644:-	Distant Proximity	1	2	0.1233715	0.0042115	Inf	0.9922129
CRYBB2P1–GRK3	22:25459515:+_22:25644592:+	Neighbours	1	2	0.1233715	0.0042115	Inf	0.9922129
LINC01933–AC008571.2	5:152267355:+_5:152375103:+	Distant Proximity	1	2	0.1233715	0.0042115	Inf	0.9922129
MAPT–KANSL1	17:45978440:+_17:46094701:-	Local Inversion	2	3	0.1629334	0.0199570	Inf	0.9935909
B3GALT5–AF064860.1	21:39613067:+_21:39725838:+	Overlapping Neighbours	2	3	0.1629334	0.0199570	Inf	0.9935909
EFCAB2–KIF26B	1:245083688:+_1:245366834:+	Neighbours	13	9	0.3382355	0.1478283	Inf	0.9950241
PXDC1–AL033523.1	6:3737079:-_6:3594535:-	Neighbours	1	3	0.0813852	0.0031107	Inf	0.9984844
ELAPOR1–ELAPOR2	1:109200237:+_7:86891889:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
RAP1A–RAP1B	1:111691417:+_12:68650400:+	Inter Chromosomal	0	2	0.0000000	0.0000000	Inf	1.0000000
CROCCP2–AL391832.4	1:16625775:-_1:234814281:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
WDTC1–MYG1	1:27303795:+_12:53306680:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
RCC1–STRAP	1:28506084:+_12:15894047:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
SSBP3–SSBP2	1:54239129:-_5:81440636:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
NFIA-AS2–AC099792.1	1:60988645:-_1:60826585:-	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
AL033530.1–AL691520.1	1:68882003:+_1:69013503:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
CADM1–LRP5	11:115504271:-_11:68347847:+	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000	yes
ZNF195–RPL13A	11:3370975:-_19:49491425:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
PHF21A–LINC02489	11:46076754:-_11:46256541:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
MALAT1–DAG1	11:65505019:+_3:49530797:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
AP003071.5–MYEOV	11:69135532:+_11:69341510:+	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
CPSF6–TPTEP2-CSNK1E	12:69262562:+_22:38336706:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000	yes
SOCS2–TGFBI	12:93583028:+_5:136063638:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
TMPO–VSIG10	12:98528012:+_12:118073992:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
NAXD–ING1	13:110615647:+_13:110719229:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
TSC22D1–TSC22D2	13:44436044:-_3:150458376:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
EFCAB11–AL137230.1	14:89931541:-_14:89629136:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
AC012405.1–AC073941.1	15:46410353:+_15:46804641:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
AC037433.1–AC009654.1	15:60223070:-_15:60074935:-	Neighbours	0	3	0.0000000	0.0000000	Inf	1.0000000
AC138932.3–NOMO1	16:14953015:-_16:14894998:+	Local Inversion	0	1	0.0000000	0.0000000	Inf	1.0000000
HS3ST4–USP6NL	16:25693151:+_10:11518574:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
AC009070.1–PRMT2	16:81006734:-_21:46643535:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
SMYD4–NASP	17:1827861:-_1:45584108:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
SUZ12P1–CCDC47	17:30769965:+_17:63766194:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000	yes
MAPT–KANSL1	17:45962470:+_17:46094701:-	Local Inversion	0	1	0.0000000	0.0000000	Inf	1.0000000
RPL17-C18orf32–RPL17P12	18:49491405:-_2:146194373:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
PPP4R1–STXBP3	18:9588089:-_1:108782422:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
CNN1–CNN3	19:11549469:+_1:94898083:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
AC123912.1–ZNF493	19:21569104:-_19:21405761:+	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
HECW2–SCAMP2	2:196433132:-_15:74848645:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
ARMC9–LYRM1	2:231240041:+_16:20915556:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
TET3–DGUOK	2:74003166:+_2:73946719:+	Local Rearrangement	0	1	0.0000000	0.0000000	Inf	1.0000000
ANKRD36C–AC008268.1	2:95884173:-_2:95667917:+	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
SNAP25–SRP72	20:10284772:+_4:56500536:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
EPB41L1–EPB41L2	20:36187763:+_6:130895119:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
AL031686.1–ZNF334	20:46474501:-_20:46504740:-	Local Rearrangement	0	1	0.0000000	0.0000000	Inf	1.0000000
CRYBB2P1–GRK3	22:25520733:+_22:25661576:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
METTL6–SH3BP5	3:15424955:-_3:15304231:-	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
GPR149–DHX36	3:154421039:-_3:154316163:-	Neighbours	0	2	0.0000000	0.0000000	Inf	1.0000000
CCNL1–SMARCA2	3:157157023:-_9:2181571:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
RPL22L1–RPL22P22	3:170868013:-_X:82506571:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
FGF12–AC092966.1	3:192727181:-_3:193169464:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
ZNF589–FBXW12	3:48260939:+_3:48373308:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000	yes
ERC2–WNT5A	3:55606780:-_3:55480918:-	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
CAMK2D–PBX3	4:113509638:-_9:125960684:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
AC053527.2–ALB	4:73302385:+_4:73404369:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
SEPTIN11–CCNI	4:76949930:+_4:77066405:-	Local Inversion	0	1	0.0000000	0.0000000	Inf	1.0000000
LINC02200–APC	5:112632097:+_5:112754873:+	Neighbours	0	2	0.0000000	0.0000000	Inf	1.0000000
CTNNA1–CTNNA2	5:138887642:+_2:80544982:+	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
AC114316.1–NR2F1-AS1	5:92204684:+_5:93563463:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
AC026780.2–NR2F1-AS1	5:92688120:-_5:93411509:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
SNAP91–PICALM	6:83707798:-_11:86031611:-	Inter Chromosomal	0	1	0.0000000	0.0000000	Inf	1.0000000
HOXA1–SKAP2	7:27095261:-_7:26844137:-	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
AC211476.4–CCDC146	7:73072670:+_7:77236947:+	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
XKR6–LONRF1	8:10924634:-_8:12743282:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000
CASC9–AC100782.1	8:75324221:-_8:75029459:-	Neighbours	0	3	0.0000000	0.0000000	Inf	1.0000000
STK3–VPS13B	8:98749276:-_8:99134632:+	Local Inversion	0	2	0.0000000	0.0000000	Inf	1.0000000
ZNF483–ECPAS	9:111534353:+_9:111444494:-	Local Inversion	0	1	0.0000000	0.0000000	Inf	1.0000000	yes
ABHD17B–CEMIP2	9:71862551:-_9:71750385:-	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
AC073529.1–MID1	X:11055936:-_X:10567603:-	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
CLIC2–AC234781.1	X:155334371:-_X:155335249:-	Local Rearrangement	0	1	0.0000000	0.0000000	Inf	1.0000000
AL050309.1–KLF8	X:56014961:+_X:56265180:+	Neighbours	0	1	0.0000000	0.0000000	Inf	1.0000000
NLGN4X–PUDP	X:6029280:-_X:6706466:-	Distant Proximity	0	1	0.0000000	0.0000000	Inf	1.0000000

Fig S13 Variants & fusion types & genetic groups

Variants

Base substitution proportions

base_substitutions.als_ctrl.sd <- plot_spectrum(type_occurrences, by = answerals.metadata$condition, CT = FALSE, indv_points = TRUE, error_bars = "stdev") # error bars SD rather than 95% CI
base_substitutions.als_ctrl.sd

tb_per_sample <- type_occurrences[, seq_len(6)] %>% tibble::rownames_to_column("sample") %>% 
        dplyr::mutate(condition = factor(toupper(answerals.metadata$condition), levels = c("CTRL","ALS"))) %>% tidyr::pivot_longer(c(-sample, 
        -condition), names_to = "variable", values_to = "nmuts") %>% 
        dplyr::group_by(sample) %>% dplyr::mutate(value = nmuts/sum(nmuts)) %>% 
        dplyr::ungroup() %>% dplyr::mutate(sub_type = stringr::str_remove(variable, 
        " .*"), variable = factor(variable, levels = unique(variable)))
tb <- tb_per_sample %>% dplyr::group_by(condition, variable) %>% dplyr::summarise(sub_type = sub_type[[1]], 
        mean = mean(value), stdev = stats::sd(value), total_individuals = sum(value), 
        total_mutations = sum(nmuts)) %>% dplyr::mutate(total_individuals = sum(total_individuals), 
        total_mutations = sum(total_mutations)) %>% dplyr::mutate(sem = stdev/sqrt(total_individuals), 
        error_95 = ifelse(total_individuals > 1, qt(0.975, df = total_individuals - 
            1) * sem, NA)) %>% dplyr::ungroup() %>% dplyr::mutate(total_mutations = prettyNum(total_mutations, 
        big.mark = ","), total_mutations = paste("No. mutations = ", 
        total_mutations), error_pos = mean)
tb_per_sample = left_join(tb_per_sample, tb[, c("condition", "sub_type", "total_mutations")], by = c("condition", "sub_type"))
base_substitutions.als_ctrl.sd.type <- ggplot(data = tb_per_sample, aes(x = condition, y = value, colour = condition)) + 
    ggrastr::rasterise(geom_violin(adjust = 1), dpi = 72) +
    ggrastr::rasterise(geom_sina(size = 0.5), dpi = 72) +
    ggrastr::rasterise(geom_boxplot(aes(fill = condition), colour = "black", width=0.2, alpha = 0.5, outlier.shape = NA), dpi = 72) + 
    scale_colour_manual(values = c("dodgerblue2", "firebrick2")) + scale_fill_manual(values = c("dodgerblue2", "firebrick2")) +
    theme_bw() + xlab("") + ylab("Relative proportions") + 
    theme_oz() + theme(panel.spacing.x = unit(0.5, "lines"), legend.position = "none") + 
    facet_wrap(~ sub_type, scales = "free", nrow = 2)
base_substitutions.als_ctrl.sd.type

Genetic subgroups

answerals.vcfannotate.mutation = answerals.vcfannotate %>% mutate(genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic",mutation %in% c("ctrl","iso") ~ "CTRL"), genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "C9orf72", "SOD1"))) %>% #mutation == "fus" ~ "FUS","FUS",  mutation == "tardbp" ~ "TARDBP", "TARDBP", 
  drop_na(genetic_group) %>% # remove all other mutants n < 3.
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, total, genetic_group, CONDITION, total_reads, mutation) # keep 1 row per sample

answerals.metadata_genetic_subgroup = answerals.metadata %>% filter(mutation %in%  c("ctrl", "iso", "sporadic", "sod1", "c9orf72")) #"fus", "tardbp", 
answerals.vcfannotate.samples_no_variants = answerals.metadata_genetic_subgroup$dataset_sample[!answerals.metadata_genetic_subgroup$dataset_sample %in% answerals.vcfannotate.mutation$dataset_sample]
answerals.vcfannotate.mutation.totals = answerals.vcfannotate.mutation %>% distinct(dataset_sample, .keep_all = TRUE) #keep 1 row per sample for total number of variants

# generalised linear model spline
## sporadic
answerals.vcfannotate.mutation.totals.glm.sporadic = glm(data=filter(answerals.vcfannotate.mutation.totals, genetic_group %in% c("Sporadic", "CTRL")), total ~ genetic_group + rms::rcs(total_reads, 3), family = poisson)
summary(answerals.vcfannotate.mutation.totals.glm.sporadic)

## 
## Call:
## glm(formula = total ~ genetic_group + rms::rcs(total_reads, 3), 
##     family = poisson, data = filter(answerals.vcfannotate.mutation.totals, 
##         genetic_group %in% c("Sporadic", "CTRL")))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -20.781   -7.213   -2.644    2.758   48.707  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                           1.064e+01  3.849e-03 2764.35   <2e-16 ***
## genetic_groupSporadic                 1.365e-02  7.146e-04   19.11   <2e-16 ***
## rms::rcs(total_reads, 3)total_reads   4.026e-09  4.936e-11   81.55   <2e-16 ***
## rms::rcs(total_reads, 3)total_reads' -2.007e-09  5.421e-11  -37.02   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 48341  on 241  degrees of freedom
## Residual deviance: 33515  on 238  degrees of freedom
## AIC: 36626
## 
## Number of Fisher Scoring iterations: 3

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           1.064e+01  3.849e-03 2764.35   <2e-16 ***
# genetic_groupSporadic                 1.365e-02  7.146e-04   19.11   <2e-16 ***

# Violin total variant events
answerals.vcfannotate.mutation.stat <- answerals.vcfannotate.mutation %>% wilcox_test(total ~ genetic_group) %>% add_significance() %>% add_xy_position() %>% filter(group1 == "CTRL") %>%
  mutate(p.adj.signif = case_when(group2 == "Sporadic" ~ "****", TRUE ~ p.adj.signif), y.position = case_when(group2 == "Sporadic" ~ 75000, TRUE ~ y.position))
answerals.vcfannotate.mutation.violin <- violin_plot(answerals.vcfannotate.mutation.totals, color_by = "genetic_group", continuous = "total", cols = c("dodgerblue2","firebrick2","forestgreen","gold2","purple","grey"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(50000,76000), dpi = 72, ylabel = "Number of Variants") +
  stat_pvalue_manual(answerals.vcfannotate.mutation.stat, tip.length = 0.01, size = 4, hide.ns = TRUE)
answerals.vcfannotate.mutation.violin

Fusions

Fusion types

ipsc_mn_als_datasets.starfusion.fusion_type_n = ipsc_mn_als_datasets.starfusion %>% 
  add_count(dataset_sample, fusion_type) %>% # count number of fusion types per sample
  distinct(dataset_sample, fusion_type, .keep_all = TRUE) %>% select(dataset_sample, fusion_type, n, condition, dataset, total_reads) #keep 1 row per sample fusion type
# add samples with 0 fusions for each fusion type
samples_no_neighbours = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% pull(filter(ipsc_mn_als_datasets.starfusion.fusion_type_n,fusion_type == "Neighbours"),dataset_sample)]
ipsc_mn_als_datasets.paired_end.metadata.no_neighbours = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% samples_no_neighbours) %>% mutate(fusion_type = "Neighbours", n = 0) %>% 
  select(dataset_sample, n, condition, fusion_type)
samples_no_local_rearrangement = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% pull(filter(ipsc_mn_als_datasets.starfusion.fusion_type_n,fusion_type == "Local\nRearrangement"),dataset_sample)]
ipsc_mn_als_datasets.paired_end.metadata.no_local_rearrangement = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% samples_no_local_rearrangement) %>% mutate(fusion_type = "Local\nRearrangement", n = 0) %>%
  select(dataset_sample, n, condition, fusion_type)
samples_no_local_inversion = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% pull(filter(ipsc_mn_als_datasets.starfusion.fusion_type_n,fusion_type == "Local\nInversion"),dataset_sample)]
ipsc_mn_als_datasets.paired_end.metadata.no_local_inversion = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% samples_no_local_inversion) %>% mutate(fusion_type = "Local\nInversion", n = 0) %>% 
  select(dataset_sample, n, condition, fusion_type)
samples_no_overlapping_neighbours = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% pull(filter(ipsc_mn_als_datasets.starfusion.fusion_type_n,fusion_type == "Overlapping\nNeighbours"),dataset_sample)]
ipsc_mn_als_datasets.paired_end.metadata.no_overlapping_neighbours = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% samples_no_overlapping_neighbours) %>% mutate(fusion_type = "Overlapping\nNeighbours", n = 0) %>% 
  select(dataset_sample, n, condition, fusion_type)
samples_no_distant_proximity = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% pull(filter(ipsc_mn_als_datasets.starfusion.fusion_type_n,fusion_type == "Distant\nProximity" ),dataset_sample)]
ipsc_mn_als_datasets.paired_end.metadata.no_distant_proximity = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% samples_no_distant_proximity) %>% mutate(fusion_type = "Distant\nProximity" , n = 0) %>% 
  select(dataset_sample, n, condition, fusion_type)
samples_no_inter_chromosomal = ipsc_mn_als_datasets.paired_end.metadata$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata$dataset_sample %in% pull(filter(ipsc_mn_als_datasets.starfusion.fusion_type_n,fusion_type == "Inter\nChromosomal"),dataset_sample)]
ipsc_mn_als_datasets.paired_end.metadata.no_inter_chromosomal = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset_sample %in% samples_no_inter_chromosomal) %>% mutate(fusion_type = "Inter\nChromosomal", n = 0) %>% 
  select(dataset_sample, n, condition, fusion_type)
ipsc_mn_als_datasets.starfusion.fusion_type_n = bind_rows(ipsc_mn_als_datasets.starfusion.fusion_type_n, ipsc_mn_als_datasets.paired_end.metadata.no_neighbours, ipsc_mn_als_datasets.paired_end.metadata.no_local_rearrangement, ipsc_mn_als_datasets.paired_end.metadata.no_local_inversion, 
                                                          ipsc_mn_als_datasets.paired_end.metadata.no_overlapping_neighbours, ipsc_mn_als_datasets.paired_end.metadata.no_distant_proximity, ipsc_mn_als_datasets.paired_end.metadata.no_inter_chromosomal) %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), 
         fusion_type = factor(fusion_type, levels = c("Local\nInversion", "Local\nRearrangement", "Neighbours", "Overlapping\nNeighbours", "Distant\nProximity", "Inter\nChromosomal")))

### Inter_Chromosomal
glm_starfusion_Inter_Chromosomal = glm(data = filter(ipsc_mn_als_datasets.starfusion.fusion_type_n, fusion_type == "Inter\nChromosomal"), n ~ CONDITION + dataset + rms::rcs(total_reads, 3), family=poisson)
summary(glm_starfusion_Inter_Chromosomal)

## 
## Call:
## glm(formula = n ~ CONDITION + dataset + rms::rcs(total_reads, 
##     3), family = poisson, data = filter(ipsc_mn_als_datasets.starfusion.fusion_type_n, 
##     fusion_type == "Inter\nChromosomal"))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5908  -0.3609  -0.2594   0.3255   1.6615  
## 
## Coefficients:
##                                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                          -1.547e-01  1.269e+00  -0.122 0.902967    
## CONDITIONALS                          6.354e-01  1.717e-01   3.700 0.000216 ***
## datasetcatanese                      -1.414e-01  4.205e-01  -0.336 0.736669    
## datasetdafinca.c9orf72                1.712e-01  1.099e+00   0.156 0.876214    
## datasetdafinca.tardbp                 1.176e-01  9.441e-01   0.125 0.900858    
## datasetdesantis                       2.212e-01  8.711e-01   0.254 0.799528    
## datasetkiskinis                       2.404e+00  1.037e+00   2.317 0.020488 *  
## datasetneurolincs.diMN               -3.735e-02  3.225e-01  -0.116 0.907791    
## datasetneurolincs.iMN                -4.341e-01  7.340e-01  -0.591 0.554279    
## datasetwang                           1.406e+00  7.545e-01   1.863 0.062466 .  
## rms::rcs(total_reads, 3)total_reads  -3.379e-10  2.106e-08  -0.016 0.987194    
## rms::rcs(total_reads, 3)total_reads' -3.008e-09  1.343e-08  -0.224 0.822832    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 229.815  on 131  degrees of freedom
## Residual deviance:  37.707  on 120  degrees of freedom
##   (264 observations deleted due to missingness)
## AIC: 363.08
## 
## Number of Fisher Scoring iterations: 4

# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                          -1.546e-01  1.269e+00  -0.122 0.903002    
# CONDITIONALS                          6.354e-01  1.717e-01   3.700 0.000216 ***

ipsc_mn_als_datasets.starfusion.fusion_type.stat <- ipsc_mn_als_datasets.starfusion.fusion_type_n %>% group_by(fusion_type) %>% t_test(n ~ CONDITION) %>% mutate(p.signif = c("ns","ns","ns","ns","ns","***"), y.position = 24) 

ipsc_mn_als_datasets.starfusion.fusion_type.violin <- ggplot(ipsc_mn_als_datasets.starfusion.fusion_type_n, aes(x = CONDITION, y = n, colour = CONDITION)) +
    # ggrastr::rasterise(geom_violin(adjust = 2), dpi = 72) +
    ggrastr::rasterise(geom_sina(size = 0.5), dpi = 72) +
    ggrastr::rasterise(geom_boxplot(aes(fill = CONDITION), colour = "black", width=0.2, alpha = 0.5, outlier.shape = NA), dpi = 72) + 
    scale_colour_manual(values = c("dodgerblue2", "firebrick2")) + scale_fill_manual(values = c("dodgerblue2", "firebrick2")) +
    theme_oz() + theme(legend.position = "none", plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5), panel.border = element_blank(), axis.ticks = element_line(colour = "black") ) +
    facet_wrap(~ fusion_type, scales = "free", nrow = 2)  + labs(y = "Number of Fusion Events", x = "") +
    stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.fusion_type.stat, label = "p.signif", tip.length = 0.01, size = 4, hide.ns = TRUE) 
ipsc_mn_als_datasets.starfusion.fusion_type.violin

# histogram
ipsc_mn_als_datasets.starfusion.fusion_type.histogram = ggplot(ipsc_mn_als_datasets.starfusion.fusion_type_n, aes(x=n, color=CONDITION, fill=CONDITION)) +
  geom_histogram(alpha=0.5, position="identity", binwidth = 1)+
  scale_color_manual(values=c("dodgerblue2", "firebrick2"))+ scale_fill_manual(values=c("dodgerblue2", "firebrick2"))+
  facet_wrap(~ fusion_type, scales = "free", nrow = 2) + theme_oz() #+ ggtitle("Zeros filled")
ipsc_mn_als_datasets.starfusion.fusion_type.histogram

Genetic subgroups

# Sporadic
ipsc_mn_als_datasets.starfusion.sporadic = read_csv(here(proj_path,"rnafusion/ipsc_mn_als_datasets.starfusion.csv")) %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), fusion_type = factor(fusion_type, levels = c("Local\nInversion", "Local\nRearrangement", "Neighbours", "Overlapping\nNeighbours", "Distant\nProximity", "Inter\nChromosomal")),
         genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic", mutation == "fus" ~ "FUS", mutation == "tardbp" ~ "TARDBP", mutation %in% c("ctrl","iso") ~ "CTRL"), 
         genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "SOD1", "C9orf72", "FUS", "TARDBP"))) %>%
  filter(dataset %in% c("answerals","neurolincs.diMN"), mutation %in% c("ctrl","iso","sporadic")) %>% left_join(multiqc_rseqc_read_distribution.tsv)

ipsc_mn_als_datasets.starfusion.sporadic.n = ipsc_mn_als_datasets.starfusion.sporadic %>% 
  add_count(dataset_sample) %>% # count number of fusion per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, n, CONDITION, genetic_group, mutation, dataset, total_reads) #keep 1 row per sample
ipsc_mn_als_datasets.starfusion.sporadic.n.stats = tibble(group1 = "CTRL", group2 = "Sporadic", p.signif = "ns", y.position = 40, xmin = 1, xmax = 2)
ipsc_mn_als_datasets.starfusion.sporadic.violin <- violin_plot(ipsc_mn_als_datasets.starfusion.sporadic.n, color_by = "genetic_group", continuous = "n", cols = c("dodgerblue2", "firebrick2"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(0,43), dpi = 72, ylabel = "Number of Fusion Events") +
  stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.sporadic.n.stats, tip.length = 0.01, size = 4, hide.ns = TRUE)

# C9orf72
ipsc_mn_als_datasets.starfusion.c9orf72 = read_csv(here(proj_path,"rnafusion/ipsc_mn_als_datasets.starfusion.csv")) %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), fusion_type = factor(fusion_type, levels = c("Local\nInversion", "Local\nRearrangement", "Neighbours", "Overlapping\nNeighbours", "Distant\nProximity", "Inter\nChromosomal")),
         genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic", mutation == "fus" ~ "FUS", mutation == "tardbp" ~ "TARDBP", mutation %in% c("ctrl","iso") ~ "CTRL"), 
         genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "SOD1", "C9orf72", "FUS", "TARDBP"))) %>%
  filter(dataset %in% c("answerals","neurolincs.diMN","catanese","dafinca.c9orf72","neurolincs.iMN","sommer"), mutation %in% c("ctrl","iso","c9orf72")) %>% left_join(multiqc_rseqc_read_distribution.tsv)
ipsc_mn_als_datasets.starfusion.c9orf72.n = ipsc_mn_als_datasets.starfusion.c9orf72 %>% 
  add_count(dataset_sample) %>% # count number of fusion per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, n, CONDITION, genetic_group,  mutation, dataset, total_reads) #keep 1 row per sample
 # add samples with 0 fusions

# glm_starfusion.c9orf72 = glm(data = ipsc_mn_als_datasets.starfusion.c9orf72.n, n ~ genetic_group + dataset + rms::rcs(total_reads, 3), family=poisson)
# summary(glm_starfusion.c9orf72)
# Coefficients:
#                                        Estimate Std. Error z value Pr(>|z|)    
# (Intercept)                           2.783e+00  5.216e-01   5.336 9.50e-08 ***
# CONDITIONALS                          2.401e-01  6.037e-02   3.978 6.96e-05 ***

# Violin
ipsc_mn_als_datasets.starfusion.c9orf72.n.stats = tibble(group1 = "CTRL", group2 = "C9orf72", p.signif = "****", y.position = 40, xmin = 1, xmax = 2)
ipsc_mn_als_datasets.starfusion.c9orf72.violin <- violin_plot(ipsc_mn_als_datasets.starfusion.c9orf72.n, color_by = "genetic_group", continuous = "n", cols = c("dodgerblue2", "forestgreen"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(0,43), dpi = 72, ylabel = "Number of Unique Fusion Events") +
  stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.c9orf72.n.stats, tip.length = 0.01, size = 4, hide.ns = TRUE)


# TARDBP
ipsc_mn_als_datasets.starfusion.tardbp = read_csv(here(proj_path,"rnafusion/ipsc_mn_als_datasets.starfusion.csv")) %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), fusion_type = factor(fusion_type, levels = c("Local\nInversion", "Local\nRearrangement", "Neighbours", "Overlapping\nNeighbours", "Distant\nProximity", "Inter\nChromosomal")),
         genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic", mutation == "fus" ~ "FUS", mutation == "tardbp" ~ "TARDBP", mutation %in% c("ctrl","iso") ~ "CTRL"), 
         genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "SOD1", "C9orf72", "FUS", "TARDBP"))) %>%
  filter(dataset %in% c("answerals","dafinca.tardbp","smith"), mutation %in% c("ctrl","iso","tardbp")) %>% left_join(multiqc_rseqc_read_distribution.tsv)
ipsc_mn_als_datasets.starfusion.tardbp.n = ipsc_mn_als_datasets.starfusion.tardbp %>% 
  add_count(dataset_sample) %>% # count number of fusion per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, n, CONDITION, genetic_group, mutation, dataset, total_reads) #keep 1 row per sample
 # add samples with 0 fusions
ipsc_mn_als_datasets.paired_end.metadata.tardbp = ipsc_mn_als_datasets.paired_end.metadata %>% filter(dataset %in% c("answerals","dafinca.tardbp","smith"), mutation %in% c("ctrl","iso","tardbp"))
ipsc_mn_als_datasets.starfusion.samples_no_fusions.tardbp = ipsc_mn_als_datasets.paired_end.metadata.tardbp$dataset_sample[!ipsc_mn_als_datasets.paired_end.metadata.tardbp$dataset_sample %in% ipsc_mn_als_datasets.starfusion.tardbp.n$dataset_sample] 
ipsc_mn_als_datasets.paired_end.metadata.no_fusions.tardbp = ipsc_mn_als_datasets.paired_end.metadata.tardbp %>% filter(dataset_sample %in% ipsc_mn_als_datasets.starfusion.samples_no_fusions.tardbp) %>% 
  left_join(multiqc_rseqc_read_distribution.tsv) %>%
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), n = 0, genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic", mutation == "fus" ~ "FUS", mutation == "tardbp" ~ "TARDBP", mutation %in% c("ctrl","iso") ~ "CTRL"), 
         genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "SOD1", "C9orf72", "FUS", "TARDBP"))) %>% select(dataset_sample, n, CONDITION, mutation, dataset, total_reads, genetic_group)
ipsc_mn_als_datasets.starfusion.tardbp.n = bind_rows(ipsc_mn_als_datasets.starfusion.tardbp.n, ipsc_mn_als_datasets.paired_end.metadata.no_fusions.tardbp)

# Violin
ipsc_mn_als_datasets.starfusion.tardbp.n.stats = tibble(group1 = "CTRL", group2 = "TARDBP", p.signif = "ns", y.position = 40, xmin = 1, xmax = 2)
ipsc_mn_als_datasets.starfusion.tardbp.violin <- violin_plot(ipsc_mn_als_datasets.starfusion.tardbp.n, color_by = "genetic_group", continuous = "n", cols = c("dodgerblue2", "purple"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(0,43), dpi = 72, ylabel = "Number of Fusion Events") +
  stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.tardbp.n.stats, tip.length = 0.01, size = 4, hide.ns = TRUE)

# FUS
ipsc_mn_als_datasets.starfusion.fus = read_csv(here(proj_path,"rnafusion/ipsc_mn_als_datasets.starfusion.csv")) %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), fusion_type = factor(fusion_type, levels = c("Local\nInversion", "Local\nRearrangement", "Neighbours", "Overlapping\nNeighbours", "Distant\nProximity", "Inter\nChromosomal")),
         genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic", mutation == "fus" ~ "FUS", mutation == "tardbp" ~ "TARDBP", mutation %in% c("ctrl","iso") ~ "CTRL"), 
         genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "SOD1", "C9orf72", "FUS", "TARDBP"))) %>%
  filter(dataset %in% c("catanese","desantis"), mutation %in% c("ctrl","iso","fus")) %>% left_join(multiqc_rseqc_read_distribution.tsv)
ipsc_mn_als_datasets.starfusion.fus.n = ipsc_mn_als_datasets.starfusion.fus %>% 
  add_count(dataset_sample) %>% # count number of fusion per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, n, CONDITION, genetic_group, mutation, dataset, total_reads) #keep 1 row per sample
 # add samples with 0 fusions

# Violin
ipsc_mn_als_datasets.starfusion.fus.n.stats = tibble(group1 = "CTRL", group2 = "FUS", p.signif = "ns", y.position = 40, xmin = 1, xmax = 2)
ipsc_mn_als_datasets.starfusion.fus.violin <- violin_plot(ipsc_mn_als_datasets.starfusion.fus.n, color_by = "genetic_group", continuous = "n", cols = c("dodgerblue2", "grey"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(0,43), dpi = 72, ylabel = "Number of Fusion Events") +
  stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.fus.n.stats, tip.length = 0.01, size = 4, hide.ns = TRUE)

# SOD1
ipsc_mn_als_datasets.starfusion.sod1 = read_csv(here(proj_path,"rnafusion/ipsc_mn_als_datasets.starfusion.csv")) %>% 
  mutate(CONDITION = factor(toupper(condition), levels = c("CTRL","ALS")), fusion_type = factor(fusion_type, levels = c("Local\nInversion", "Local\nRearrangement", "Neighbours", "Overlapping\nNeighbours", "Distant\nProximity", "Inter\nChromosomal")),
         genetic_group = case_when(mutation == "c9orf72" ~ "C9orf72", mutation == "sod1" ~ "SOD1", mutation == "sporadic" ~ "Sporadic", mutation == "fus" ~ "FUS", mutation == "tardbp" ~ "TARDBP", mutation %in% c("ctrl","iso") ~ "CTRL"), 
         genetic_group = factor(genetic_group, levels = c("CTRL", "Sporadic", "SOD1", "C9orf72", "FUS", "TARDBP"))) %>%
  filter(dataset %in% c("answerals","neurolincs.diMN", "kiskinis","wang"), mutation %in% c("ctrl","iso","sod1")) %>% left_join(multiqc_rseqc_read_distribution.tsv)
ipsc_mn_als_datasets.starfusion.sod1.n = ipsc_mn_als_datasets.starfusion.sod1 %>% 
  add_count(dataset_sample) %>% # count number of fusion per sample
  distinct(dataset_sample, .keep_all = TRUE) %>% select(dataset_sample, n, CONDITION, genetic_group, mutation, dataset, total_reads) #keep 1 row per sample
 # add samples with 0 fusions

# Violin
ipsc_mn_als_datasets.starfusion.sod1.n.stats = tibble(group1 = "CTRL", group2 = "SOD1", p.signif = "****", y.position = 40, xmin = 1, xmax = 2)
ipsc_mn_als_datasets.starfusion.sod1.violin <- violin_plot(ipsc_mn_als_datasets.starfusion.sod1.n, color_by = "genetic_group", continuous = "n", cols = c("dodgerblue2", "gold2"), plot_stats = FALSE, arrow_labels = FALSE, ylims = c(0,43), dpi = 72, ylabel = "Number of Fusion Events") +
  stat_pvalue_manual(ipsc_mn_als_datasets.starfusion.sod1.n.stats, tip.length = 0.01, size = 4, hide.ns = TRUE)

plot_grid(ipsc_mn_als_datasets.starfusion.sporadic.violin, ipsc_mn_als_datasets.starfusion.c9orf72.violin, ipsc_mn_als_datasets.starfusion.sod1.violin, ipsc_mn_als_datasets.starfusion.tardbp.violin, ipsc_mn_als_datasets.starfusion.fus.violin, nrow = 2)